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Merge 84128a569e3d21b132163ed8ff713262680a0441 into 453c6e38a51e9d1d5a2aa5fb7f1014a711913397
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__version__ = "8.1.34"
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from ultralytics.data.explorer.explorer import Explorer
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from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld, YOLOv10
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from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld, YOLOv10,YOLOv10Seg
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from ultralytics.models.fastsam import FastSAM
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from ultralytics.models.nas import NAS
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from ultralytics.utils import ASSETS, SETTINGS as settings
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Argoverse-HD dataset (ring-front-center camera) https://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
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# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
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# Example usage: yolo train data=Argoverse.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── Argoverse ← downloads here (31.5 GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/Argoverse # dataset root dir
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train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
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val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
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test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: bus
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5: truck
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6: traffic_light
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7: stop_sign
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import json
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from tqdm import tqdm
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from ultralytics.utils.downloads import download
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from pathlib import Path
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def argoverse2yolo(set):
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labels = {}
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a = json.load(open(set, "rb"))
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for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
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img_id = annot['image_id']
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img_name = a['images'][img_id]['name']
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img_label_name = f'{img_name[:-3]}txt'
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cls = annot['category_id'] # instance class id
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x_center, y_center, width, height = annot['bbox']
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x_center = (x_center + width / 2) / 1920.0 # offset and scale
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y_center = (y_center + height / 2) / 1200.0 # offset and scale
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width /= 1920.0 # scale
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height /= 1200.0 # scale
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img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
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if not img_dir.exists():
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img_dir.mkdir(parents=True, exist_ok=True)
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k = str(img_dir / img_label_name)
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if k not in labels:
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labels[k] = []
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labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
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for k in labels:
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with open(k, "w") as f:
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f.writelines(labels[k])
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# Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link']
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print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
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print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
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# download(urls, dir=dir)
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# Convert
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annotations_dir = 'Argoverse-HD/annotations/'
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(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
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for d in "train.json", "val.json":
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argoverse2yolo(dir / annotations_dir / d) # convert Argoverse annotations to YOLO labels
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@ -1,36 +0,0 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# DOTA 1.5 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
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# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
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# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.5.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── dota1.5 ← downloads here (2GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/DOTAv1.5 # dataset root dir
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train: images/train # train images (relative to 'path') 1411 images
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val: images/val # val images (relative to 'path') 458 images
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test: images/test # test images (optional) 937 images
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# Classes for DOTA 1.5
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names:
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0: plane
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1: ship
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2: storage tank
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3: baseball diamond
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4: tennis court
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5: basketball court
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6: ground track field
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7: harbor
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8: bridge
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9: large vehicle
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10: small vehicle
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11: helicopter
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12: roundabout
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13: soccer ball field
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14: swimming pool
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15: container crane
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# Download script/URL (optional)
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download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.5.zip
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
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# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
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# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── dota1 ← downloads here (2GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/DOTAv1 # dataset root dir
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train: images/train # train images (relative to 'path') 1411 images
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val: images/val # val images (relative to 'path') 458 images
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test: images/test # test images (optional) 937 images
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# Classes for DOTA 1.0
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names:
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0: plane
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1: ship
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2: storage tank
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3: baseball diamond
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4: tennis court
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5: basketball court
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6: ground track field
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7: harbor
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8: bridge
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9: large vehicle
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10: small vehicle
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11: helicopter
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12: roundabout
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13: soccer ball field
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14: swimming pool
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# Download script/URL (optional)
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download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.zip
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
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# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
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# Example usage: yolo train data=GlobalWheat2020.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── GlobalWheat2020 ← downloads here (7.0 GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/GlobalWheat2020 # dataset root dir
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train: # train images (relative to 'path') 3422 images
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- images/arvalis_1
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- images/arvalis_2
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- images/arvalis_3
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- images/ethz_1
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- images/rres_1
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- images/inrae_1
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- images/usask_1
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val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
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- images/ethz_1
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test: # test images (optional) 1276 images
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- images/utokyo_1
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- images/utokyo_2
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- images/nau_1
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- images/uq_1
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# Classes
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names:
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0: wheat_head
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from ultralytics.utils.downloads import download
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from pathlib import Path
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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download(urls, dir=dir)
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# Make Directories
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for p in 'annotations', 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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(dir / 'global-wheat-codalab-official' / p).rename(dir / 'images' / p) # move to /images
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f = (dir / 'global-wheat-codalab-official' / p).with_suffix('.json') # json file
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if f.exists():
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f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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File diff suppressed because it is too large
Load Diff
@ -1,442 +0,0 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Objects365 dataset https://www.objects365.org/ by Megvii
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# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
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# Example usage: yolo train data=Objects365.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/Objects365 # dataset root dir
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train: images/train # train images (relative to 'path') 1742289 images
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val: images/val # val images (relative to 'path') 80000 images
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test: # test images (optional)
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# Classes
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names:
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0: Person
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1: Sneakers
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2: Chair
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3: Other Shoes
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4: Hat
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5: Car
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6: Lamp
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7: Glasses
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8: Bottle
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9: Desk
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10: Cup
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11: Street Lights
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12: Cabinet/shelf
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13: Handbag/Satchel
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14: Bracelet
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15: Plate
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16: Picture/Frame
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17: Helmet
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18: Book
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19: Gloves
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20: Storage box
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21: Boat
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22: Leather Shoes
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23: Flower
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24: Bench
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25: Potted Plant
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26: Bowl/Basin
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27: Flag
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28: Pillow
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29: Boots
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30: Vase
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31: Microphone
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32: Necklace
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33: Ring
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34: SUV
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35: Wine Glass
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36: Belt
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37: Monitor/TV
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38: Backpack
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39: Umbrella
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40: Traffic Light
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41: Speaker
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42: Watch
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43: Tie
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44: Trash bin Can
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45: Slippers
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46: Bicycle
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47: Stool
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48: Barrel/bucket
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49: Van
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50: Couch
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51: Sandals
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52: Basket
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53: Drum
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54: Pen/Pencil
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55: Bus
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56: Wild Bird
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57: High Heels
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58: Motorcycle
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59: Guitar
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60: Carpet
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61: Cell Phone
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62: Bread
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63: Camera
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64: Canned
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65: Truck
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66: Traffic cone
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67: Cymbal
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68: Lifesaver
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69: Towel
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70: Stuffed Toy
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71: Candle
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72: Sailboat
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73: Laptop
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74: Awning
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75: Bed
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76: Faucet
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77: Tent
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78: Horse
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79: Mirror
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80: Power outlet
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81: Sink
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82: Apple
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83: Air Conditioner
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84: Knife
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85: Hockey Stick
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86: Paddle
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87: Pickup Truck
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88: Fork
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89: Traffic Sign
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90: Balloon
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91: Tripod
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92: Dog
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93: Spoon
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94: Clock
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95: Pot
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96: Cow
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97: Cake
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98: Dinning Table
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99: Sheep
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100: Hanger
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101: Blackboard/Whiteboard
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102: Napkin
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103: Other Fish
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104: Orange/Tangerine
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105: Toiletry
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106: Keyboard
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107: Tomato
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108: Lantern
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109: Machinery Vehicle
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110: Fan
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111: Green Vegetables
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112: Banana
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113: Baseball Glove
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114: Airplane
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115: Mouse
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116: Train
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117: Pumpkin
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118: Soccer
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119: Skiboard
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120: Luggage
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121: Nightstand
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122: Tea pot
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123: Telephone
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124: Trolley
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125: Head Phone
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126: Sports Car
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127: Stop Sign
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128: Dessert
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129: Scooter
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130: Stroller
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131: Crane
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132: Remote
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133: Refrigerator
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134: Oven
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135: Lemon
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136: Duck
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137: Baseball Bat
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138: Surveillance Camera
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139: Cat
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140: Jug
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141: Broccoli
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142: Piano
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143: Pizza
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144: Elephant
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145: Skateboard
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146: Surfboard
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147: Gun
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148: Skating and Skiing shoes
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149: Gas stove
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150: Donut
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151: Bow Tie
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152: Carrot
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153: Toilet
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154: Kite
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155: Strawberry
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156: Other Balls
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157: Shovel
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158: Pepper
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159: Computer Box
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160: Toilet Paper
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161: Cleaning Products
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162: Chopsticks
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163: Microwave
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164: Pigeon
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165: Baseball
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||||
166: Cutting/chopping Board
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167: Coffee Table
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168: Side Table
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169: Scissors
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170: Marker
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||||
171: Pie
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||||
172: Ladder
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173: Snowboard
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174: Cookies
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175: Radiator
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176: Fire Hydrant
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177: Basketball
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178: Zebra
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179: Grape
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180: Giraffe
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181: Potato
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182: Sausage
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183: Tricycle
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184: Violin
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185: Egg
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186: Fire Extinguisher
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187: Candy
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||||
188: Fire Truck
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||||
189: Billiards
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||||
190: Converter
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||||
191: Bathtub
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||||
192: Wheelchair
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||||
193: Golf Club
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194: Briefcase
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195: Cucumber
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196: Cigar/Cigarette
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197: Paint Brush
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198: Pear
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199: Heavy Truck
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200: Hamburger
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201: Extractor
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202: Extension Cord
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203: Tong
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204: Tennis Racket
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205: Folder
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206: American Football
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207: earphone
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208: Mask
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209: Kettle
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||||
210: Tennis
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||||
211: Ship
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212: Swing
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213: Coffee Machine
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214: Slide
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||||
215: Carriage
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216: Onion
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217: Green beans
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218: Projector
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219: Frisbee
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220: Washing Machine/Drying Machine
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221: Chicken
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222: Printer
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||||
223: Watermelon
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224: Saxophone
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225: Tissue
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226: Toothbrush
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||||
227: Ice cream
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||||
228: Hot-air balloon
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||||
229: Cello
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||||
230: French Fries
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||||
231: Scale
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||||
232: Trophy
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||||
233: Cabbage
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||||
234: Hot dog
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||||
235: Blender
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||||
236: Peach
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||||
237: Rice
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||||
238: Wallet/Purse
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239: Volleyball
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240: Deer
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241: Goose
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||||
242: Tape
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||||
243: Tablet
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||||
244: Cosmetics
|
||||
245: Trumpet
|
||||
246: Pineapple
|
||||
247: Golf Ball
|
||||
248: Ambulance
|
||||
249: Parking meter
|
||||
250: Mango
|
||||
251: Key
|
||||
252: Hurdle
|
||||
253: Fishing Rod
|
||||
254: Medal
|
||||
255: Flute
|
||||
256: Brush
|
||||
257: Penguin
|
||||
258: Megaphone
|
||||
259: Corn
|
||||
260: Lettuce
|
||||
261: Garlic
|
||||
262: Swan
|
||||
263: Helicopter
|
||||
264: Green Onion
|
||||
265: Sandwich
|
||||
266: Nuts
|
||||
267: Speed Limit Sign
|
||||
268: Induction Cooker
|
||||
269: Broom
|
||||
270: Trombone
|
||||
271: Plum
|
||||
272: Rickshaw
|
||||
273: Goldfish
|
||||
274: Kiwi fruit
|
||||
275: Router/modem
|
||||
276: Poker Card
|
||||
277: Toaster
|
||||
278: Shrimp
|
||||
279: Sushi
|
||||
280: Cheese
|
||||
281: Notepaper
|
||||
282: Cherry
|
||||
283: Pliers
|
||||
284: CD
|
||||
285: Pasta
|
||||
286: Hammer
|
||||
287: Cue
|
||||
288: Avocado
|
||||
289: Hamimelon
|
||||
290: Flask
|
||||
291: Mushroom
|
||||
292: Screwdriver
|
||||
293: Soap
|
||||
294: Recorder
|
||||
295: Bear
|
||||
296: Eggplant
|
||||
297: Board Eraser
|
||||
298: Coconut
|
||||
299: Tape Measure/Ruler
|
||||
300: Pig
|
||||
301: Showerhead
|
||||
302: Globe
|
||||
303: Chips
|
||||
304: Steak
|
||||
305: Crosswalk Sign
|
||||
306: Stapler
|
||||
307: Camel
|
||||
308: Formula 1
|
||||
309: Pomegranate
|
||||
310: Dishwasher
|
||||
311: Crab
|
||||
312: Hoverboard
|
||||
313: Meat ball
|
||||
314: Rice Cooker
|
||||
315: Tuba
|
||||
316: Calculator
|
||||
317: Papaya
|
||||
318: Antelope
|
||||
319: Parrot
|
||||
320: Seal
|
||||
321: Butterfly
|
||||
322: Dumbbell
|
||||
323: Donkey
|
||||
324: Lion
|
||||
325: Urinal
|
||||
326: Dolphin
|
||||
327: Electric Drill
|
||||
328: Hair Dryer
|
||||
329: Egg tart
|
||||
330: Jellyfish
|
||||
331: Treadmill
|
||||
332: Lighter
|
||||
333: Grapefruit
|
||||
334: Game board
|
||||
335: Mop
|
||||
336: Radish
|
||||
337: Baozi
|
||||
338: Target
|
||||
339: French
|
||||
340: Spring Rolls
|
||||
341: Monkey
|
||||
342: Rabbit
|
||||
343: Pencil Case
|
||||
344: Yak
|
||||
345: Red Cabbage
|
||||
346: Binoculars
|
||||
347: Asparagus
|
||||
348: Barbell
|
||||
349: Scallop
|
||||
350: Noddles
|
||||
351: Comb
|
||||
352: Dumpling
|
||||
353: Oyster
|
||||
354: Table Tennis paddle
|
||||
355: Cosmetics Brush/Eyeliner Pencil
|
||||
356: Chainsaw
|
||||
357: Eraser
|
||||
358: Lobster
|
||||
359: Durian
|
||||
360: Okra
|
||||
361: Lipstick
|
||||
362: Cosmetics Mirror
|
||||
363: Curling
|
||||
364: Table Tennis
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
from ultralytics.utils.downloads import download
|
||||
from ultralytics.utils.ops import xyxy2xywhn
|
||||
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
check_requirements(('pycocotools>=2.0',))
|
||||
from pycocotools.coco import COCO
|
||||
|
||||
# Make Directories
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
for p in 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
for q in 'train', 'val':
|
||||
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Train, Val Splits
|
||||
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
|
||||
print(f"Processing {split} in {patches} patches ...")
|
||||
images, labels = dir / 'images' / split, dir / 'labels' / split
|
||||
|
||||
# Download
|
||||
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
|
||||
if split == 'train':
|
||||
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir) # annotations json
|
||||
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, threads=8)
|
||||
elif split == 'val':
|
||||
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir) # annotations json
|
||||
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, threads=8)
|
||||
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, threads=8)
|
||||
|
||||
# Move
|
||||
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
|
||||
f.rename(images / f.name) # move to /images/{split}
|
||||
|
||||
# Labels
|
||||
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
|
||||
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
||||
for cid, cat in enumerate(names):
|
||||
catIds = coco.getCatIds(catNms=[cat])
|
||||
imgIds = coco.getImgIds(catIds=catIds)
|
||||
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
||||
width, height = im["width"], im["height"]
|
||||
path = Path(im["file_name"]) # image filename
|
||||
try:
|
||||
with open(labels / path.with_suffix('.txt').name, 'a') as file:
|
||||
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
|
||||
for a in coco.loadAnns(annIds):
|
||||
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
||||
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
|
||||
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
|
||||
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
@ -1,57 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
|
||||
# Example usage: yolo train data=SKU-110K.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── SKU-110K ← downloads here (13.6 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/SKU-110K # dataset root dir
|
||||
train: train.txt # train images (relative to 'path') 8219 images
|
||||
val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: object
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.utils.downloads import download
|
||||
from ultralytics.utils.ops import xyxy2xywh
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
parent = Path(dir.parent) # download dir
|
||||
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
|
||||
download(urls, dir=parent)
|
||||
|
||||
# Rename directories
|
||||
if dir.exists():
|
||||
shutil.rmtree(dir)
|
||||
(parent / 'SKU110K_fixed').rename(dir) # rename dir
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
|
||||
|
||||
# Convert labels
|
||||
names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
|
||||
for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
|
||||
x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
|
||||
images, unique_images = x[:, 0], np.unique(x[:, 0])
|
||||
with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
|
||||
f.writelines(f'./images/{s}\n' for s in unique_images)
|
||||
for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
|
||||
cls = 0 # single-class dataset
|
||||
with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
|
||||
for r in x[images == im]:
|
||||
w, h = r[6], r[7] # image width, height
|
||||
xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
|
||||
f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
|
||||
@ -1,99 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
||||
# Documentation: # Documentation: https://docs.ultralytics.com/datasets/detect/voc/
|
||||
# Example usage: yolo train data=VOC.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── VOC ← downloads here (2.8 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VOC
|
||||
train: # train images (relative to 'path') 16551 images
|
||||
- images/train2012
|
||||
- images/train2007
|
||||
- images/val2012
|
||||
- images/val2007
|
||||
val: # val images (relative to 'path') 4952 images
|
||||
- images/test2007
|
||||
test: # test images (optional)
|
||||
- images/test2007
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: aeroplane
|
||||
1: bicycle
|
||||
2: bird
|
||||
3: boat
|
||||
4: bottle
|
||||
5: bus
|
||||
6: car
|
||||
7: cat
|
||||
8: chair
|
||||
9: cow
|
||||
10: diningtable
|
||||
11: dog
|
||||
12: horse
|
||||
13: motorbike
|
||||
14: person
|
||||
15: pottedplant
|
||||
16: sheep
|
||||
17: sofa
|
||||
18: train
|
||||
19: tvmonitor
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
from tqdm import tqdm
|
||||
from ultralytics.utils.downloads import download
|
||||
from pathlib import Path
|
||||
|
||||
def convert_label(path, lb_path, year, image_id):
|
||||
def convert_box(size, box):
|
||||
dw, dh = 1. / size[0], 1. / size[1]
|
||||
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||
return x * dw, y * dh, w * dw, h * dh
|
||||
|
||||
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
|
||||
out_file = open(lb_path, 'w')
|
||||
tree = ET.parse(in_file)
|
||||
root = tree.getroot()
|
||||
size = root.find('size')
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
||||
names = list(yaml['names'].values()) # names list
|
||||
for obj in root.iter('object'):
|
||||
cls = obj.find('name').text
|
||||
if cls in names and int(obj.find('difficult').text) != 1:
|
||||
xmlbox = obj.find('bndbox')
|
||||
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
|
||||
cls_id = names.index(cls) # class id
|
||||
out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + '\n')
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', curl=True, threads=3, exist_ok=True) # download and unzip over existing paths (required)
|
||||
|
||||
# Convert
|
||||
path = dir / 'images/VOCdevkit'
|
||||
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
|
||||
imgs_path = dir / 'images' / f'{image_set}{year}'
|
||||
lbs_path = dir / 'labels' / f'{image_set}{year}'
|
||||
imgs_path.mkdir(exist_ok=True, parents=True)
|
||||
lbs_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
|
||||
image_ids = f.read().strip().split()
|
||||
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
||||
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
||||
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
||||
f.rename(imgs_path / f.name) # move image
|
||||
convert_label(path, lb_path, year, id) # convert labels to YOLO format
|
||||
@ -1,72 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
|
||||
# Example usage: yolo train data=VisDrone.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── VisDrone ← downloads here (2.3 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VisDrone # dataset root dir
|
||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: pedestrian
|
||||
1: people
|
||||
2: bicycle
|
||||
3: car
|
||||
4: van
|
||||
5: truck
|
||||
6: tricycle
|
||||
7: awning-tricycle
|
||||
8: bus
|
||||
9: motor
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.utils.downloads import download
|
||||
|
||||
def visdrone2yolo(dir):
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
def convert_box(size, box):
|
||||
# Convert VisDrone box to YOLO xywh box
|
||||
dw = 1. / size[0]
|
||||
dh = 1. / size[1]
|
||||
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
|
||||
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
|
||||
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
|
||||
for f in pbar:
|
||||
img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
|
||||
lines = []
|
||||
with open(f, 'r') as file: # read annotation.txt
|
||||
for row in [x.split(',') for x in file.read().strip().splitlines()]:
|
||||
if row[4] == '0': # VisDrone 'ignored regions' class 0
|
||||
continue
|
||||
cls = int(row[5]) - 1
|
||||
box = convert_box(img_size, tuple(map(int, row[:4])))
|
||||
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
|
||||
with open(str(f).replace(f'{os.sep}annotations{os.sep}', f'{os.sep}labels{os.sep}'), 'w') as fl:
|
||||
fl.writelines(lines) # write label.txt
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
|
||||
download(urls, dir=dir, curl=True, threads=4)
|
||||
|
||||
# Convert
|
||||
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
|
||||
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
|
||||
@ -1,24 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# African-wildlife dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/african-wildlife/
|
||||
# Example usage: yolo train data=african-wildlife.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── african-wildlife ← downloads here (100 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/african-wildlife # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 1052 images
|
||||
val: valid/images # val images (relative to 'path') 225 images
|
||||
test: test/images # test images (relative to 'path') 227 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: buffalo
|
||||
1: elephant
|
||||
2: rhino
|
||||
3: zebra
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/african-wildlife.zip
|
||||
@ -1,22 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Brain-tumor dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
|
||||
# Example usage: yolo train data=brain-tumor.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── brain-tumor ← downloads here (4.05 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/brain-tumor # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 893 images
|
||||
val: valid/images # val images (relative to 'path') 223 images
|
||||
test: # test images (relative to 'path')
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: negative
|
||||
1: positive
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/brain-tumor.zip
|
||||
@ -1,43 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Carparts-seg dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
|
||||
# Example usage: yolo train data=carparts-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── carparts-seg ← downloads here (132 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/carparts-seg # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 3516 images
|
||||
val: valid/images # val images (relative to 'path') 276 images
|
||||
test: test/images # test images (relative to 'path') 401 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: back_bumper
|
||||
1: back_door
|
||||
2: back_glass
|
||||
3: back_left_door
|
||||
4: back_left_light
|
||||
5: back_light
|
||||
6: back_right_door
|
||||
7: back_right_light
|
||||
8: front_bumper
|
||||
9: front_door
|
||||
10: front_glass
|
||||
11: front_left_door
|
||||
12: front_left_light
|
||||
13: front_light
|
||||
14: front_right_door
|
||||
15: front_right_light
|
||||
16: hood
|
||||
17: left_mirror
|
||||
18: object
|
||||
19: right_mirror
|
||||
20: tailgate
|
||||
21: trunk
|
||||
22: wheel
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/carparts-seg.zip
|
||||
@ -1,38 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO 2017 dataset https://cocodataset.org by Microsoft
|
||||
# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
|
||||
# Example usage: yolo train data=coco-pose.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco-pose ← downloads here (20.1 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco-pose # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Keypoints
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: |
|
||||
from ultralytics.utils.downloads import download
|
||||
from pathlib import Path
|
||||
|
||||
# Download labels
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||
urls = [url + 'coco2017labels-pose.zip'] # labels
|
||||
download(urls, dir=dir.parent)
|
||||
# Download data
|
||||
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
|
||||
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
|
||||
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
|
||||
download(urls, dir=dir / 'images', threads=3)
|
||||
@ -1,114 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO 2017 dataset https://cocodataset.org by Microsoft
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
|
||||
# Example usage: yolo train data=coco.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco ← downloads here (20.1 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: |
|
||||
from ultralytics.utils.downloads import download
|
||||
from pathlib import Path
|
||||
|
||||
# Download labels
|
||||
segments = True # segment or box labels
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
|
||||
download(urls, dir=dir.parent)
|
||||
# Download data
|
||||
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
|
||||
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
|
||||
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
|
||||
download(urls, dir=dir / 'images', threads=3)
|
||||
@ -1,100 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
|
||||
# Example usage: yolo train data=coco128.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco128-seg ← downloads here (7 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128-seg # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco128-seg.zip
|
||||
@ -1,100 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
|
||||
# Example usage: yolo train data=coco128.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here (7 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128 # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco128.zip
|
||||
@ -1,25 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose/
|
||||
# Example usage: yolo train data=coco8-pose.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco8-pose ← downloads here (1 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco8-pose # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Keypoints
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco8-pose.zip
|
||||
@ -1,100 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
|
||||
# Example usage: yolo train data=coco8-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco8-seg ← downloads here (1 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco8-seg # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco8-seg.zip
|
||||
@ -1,100 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
|
||||
# Example usage: yolo train data=coco8.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── coco8 ← downloads here (1 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco8 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/coco8.zip
|
||||
@ -1,21 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Crack-seg dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
|
||||
# Example usage: yolo train data=crack-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── crack-seg ← downloads here (91.2 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/crack-seg # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 3717 images
|
||||
val: valid/images # val images (relative to 'path') 112 images
|
||||
test: test/images # test images (relative to 'path') 200 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: crack
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/crack-seg.zip
|
||||
@ -1,34 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
|
||||
# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── dota8 ← downloads here (1MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/dota8 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 4 images
|
||||
val: images/val # val images (relative to 'path') 4 images
|
||||
|
||||
# Classes for DOTA 1.0
|
||||
names:
|
||||
0: plane
|
||||
1: ship
|
||||
2: storage tank
|
||||
3: baseball diamond
|
||||
4: tennis court
|
||||
5: basketball court
|
||||
6: ground track field
|
||||
7: harbor
|
||||
8: bridge
|
||||
9: large vehicle
|
||||
10: small vehicle
|
||||
11: helicopter
|
||||
12: roundabout
|
||||
13: soccer ball field
|
||||
14: swimming pool
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/dota8.zip
|
||||
@ -1,660 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7/
|
||||
# Example usage: yolo train data=open-images-v7.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── open-images-v7 ← downloads here (561 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/open-images-v7 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1743042 images
|
||||
val: images/val # val images (relative to 'path') 41620 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Accordion
|
||||
1: Adhesive tape
|
||||
2: Aircraft
|
||||
3: Airplane
|
||||
4: Alarm clock
|
||||
5: Alpaca
|
||||
6: Ambulance
|
||||
7: Animal
|
||||
8: Ant
|
||||
9: Antelope
|
||||
10: Apple
|
||||
11: Armadillo
|
||||
12: Artichoke
|
||||
13: Auto part
|
||||
14: Axe
|
||||
15: Backpack
|
||||
16: Bagel
|
||||
17: Baked goods
|
||||
18: Balance beam
|
||||
19: Ball
|
||||
20: Balloon
|
||||
21: Banana
|
||||
22: Band-aid
|
||||
23: Banjo
|
||||
24: Barge
|
||||
25: Barrel
|
||||
26: Baseball bat
|
||||
27: Baseball glove
|
||||
28: Bat (Animal)
|
||||
29: Bathroom accessory
|
||||
30: Bathroom cabinet
|
||||
31: Bathtub
|
||||
32: Beaker
|
||||
33: Bear
|
||||
34: Bed
|
||||
35: Bee
|
||||
36: Beehive
|
||||
37: Beer
|
||||
38: Beetle
|
||||
39: Bell pepper
|
||||
40: Belt
|
||||
41: Bench
|
||||
42: Bicycle
|
||||
43: Bicycle helmet
|
||||
44: Bicycle wheel
|
||||
45: Bidet
|
||||
46: Billboard
|
||||
47: Billiard table
|
||||
48: Binoculars
|
||||
49: Bird
|
||||
50: Blender
|
||||
51: Blue jay
|
||||
52: Boat
|
||||
53: Bomb
|
||||
54: Book
|
||||
55: Bookcase
|
||||
56: Boot
|
||||
57: Bottle
|
||||
58: Bottle opener
|
||||
59: Bow and arrow
|
||||
60: Bowl
|
||||
61: Bowling equipment
|
||||
62: Box
|
||||
63: Boy
|
||||
64: Brassiere
|
||||
65: Bread
|
||||
66: Briefcase
|
||||
67: Broccoli
|
||||
68: Bronze sculpture
|
||||
69: Brown bear
|
||||
70: Building
|
||||
71: Bull
|
||||
72: Burrito
|
||||
73: Bus
|
||||
74: Bust
|
||||
75: Butterfly
|
||||
76: Cabbage
|
||||
77: Cabinetry
|
||||
78: Cake
|
||||
79: Cake stand
|
||||
80: Calculator
|
||||
81: Camel
|
||||
82: Camera
|
||||
83: Can opener
|
||||
84: Canary
|
||||
85: Candle
|
||||
86: Candy
|
||||
87: Cannon
|
||||
88: Canoe
|
||||
89: Cantaloupe
|
||||
90: Car
|
||||
91: Carnivore
|
||||
92: Carrot
|
||||
93: Cart
|
||||
94: Cassette deck
|
||||
95: Castle
|
||||
96: Cat
|
||||
97: Cat furniture
|
||||
98: Caterpillar
|
||||
99: Cattle
|
||||
100: Ceiling fan
|
||||
101: Cello
|
||||
102: Centipede
|
||||
103: Chainsaw
|
||||
104: Chair
|
||||
105: Cheese
|
||||
106: Cheetah
|
||||
107: Chest of drawers
|
||||
108: Chicken
|
||||
109: Chime
|
||||
110: Chisel
|
||||
111: Chopsticks
|
||||
112: Christmas tree
|
||||
113: Clock
|
||||
114: Closet
|
||||
115: Clothing
|
||||
116: Coat
|
||||
117: Cocktail
|
||||
118: Cocktail shaker
|
||||
119: Coconut
|
||||
120: Coffee
|
||||
121: Coffee cup
|
||||
122: Coffee table
|
||||
123: Coffeemaker
|
||||
124: Coin
|
||||
125: Common fig
|
||||
126: Common sunflower
|
||||
127: Computer keyboard
|
||||
128: Computer monitor
|
||||
129: Computer mouse
|
||||
130: Container
|
||||
131: Convenience store
|
||||
132: Cookie
|
||||
133: Cooking spray
|
||||
134: Corded phone
|
||||
135: Cosmetics
|
||||
136: Couch
|
||||
137: Countertop
|
||||
138: Cowboy hat
|
||||
139: Crab
|
||||
140: Cream
|
||||
141: Cricket ball
|
||||
142: Crocodile
|
||||
143: Croissant
|
||||
144: Crown
|
||||
145: Crutch
|
||||
146: Cucumber
|
||||
147: Cupboard
|
||||
148: Curtain
|
||||
149: Cutting board
|
||||
150: Dagger
|
||||
151: Dairy Product
|
||||
152: Deer
|
||||
153: Desk
|
||||
154: Dessert
|
||||
155: Diaper
|
||||
156: Dice
|
||||
157: Digital clock
|
||||
158: Dinosaur
|
||||
159: Dishwasher
|
||||
160: Dog
|
||||
161: Dog bed
|
||||
162: Doll
|
||||
163: Dolphin
|
||||
164: Door
|
||||
165: Door handle
|
||||
166: Doughnut
|
||||
167: Dragonfly
|
||||
168: Drawer
|
||||
169: Dress
|
||||
170: Drill (Tool)
|
||||
171: Drink
|
||||
172: Drinking straw
|
||||
173: Drum
|
||||
174: Duck
|
||||
175: Dumbbell
|
||||
176: Eagle
|
||||
177: Earrings
|
||||
178: Egg (Food)
|
||||
179: Elephant
|
||||
180: Envelope
|
||||
181: Eraser
|
||||
182: Face powder
|
||||
183: Facial tissue holder
|
||||
184: Falcon
|
||||
185: Fashion accessory
|
||||
186: Fast food
|
||||
187: Fax
|
||||
188: Fedora
|
||||
189: Filing cabinet
|
||||
190: Fire hydrant
|
||||
191: Fireplace
|
||||
192: Fish
|
||||
193: Flag
|
||||
194: Flashlight
|
||||
195: Flower
|
||||
196: Flowerpot
|
||||
197: Flute
|
||||
198: Flying disc
|
||||
199: Food
|
||||
200: Food processor
|
||||
201: Football
|
||||
202: Football helmet
|
||||
203: Footwear
|
||||
204: Fork
|
||||
205: Fountain
|
||||
206: Fox
|
||||
207: French fries
|
||||
208: French horn
|
||||
209: Frog
|
||||
210: Fruit
|
||||
211: Frying pan
|
||||
212: Furniture
|
||||
213: Garden Asparagus
|
||||
214: Gas stove
|
||||
215: Giraffe
|
||||
216: Girl
|
||||
217: Glasses
|
||||
218: Glove
|
||||
219: Goat
|
||||
220: Goggles
|
||||
221: Goldfish
|
||||
222: Golf ball
|
||||
223: Golf cart
|
||||
224: Gondola
|
||||
225: Goose
|
||||
226: Grape
|
||||
227: Grapefruit
|
||||
228: Grinder
|
||||
229: Guacamole
|
||||
230: Guitar
|
||||
231: Hair dryer
|
||||
232: Hair spray
|
||||
233: Hamburger
|
||||
234: Hammer
|
||||
235: Hamster
|
||||
236: Hand dryer
|
||||
237: Handbag
|
||||
238: Handgun
|
||||
239: Harbor seal
|
||||
240: Harmonica
|
||||
241: Harp
|
||||
242: Harpsichord
|
||||
243: Hat
|
||||
244: Headphones
|
||||
245: Heater
|
||||
246: Hedgehog
|
||||
247: Helicopter
|
||||
248: Helmet
|
||||
249: High heels
|
||||
250: Hiking equipment
|
||||
251: Hippopotamus
|
||||
252: Home appliance
|
||||
253: Honeycomb
|
||||
254: Horizontal bar
|
||||
255: Horse
|
||||
256: Hot dog
|
||||
257: House
|
||||
258: Houseplant
|
||||
259: Human arm
|
||||
260: Human beard
|
||||
261: Human body
|
||||
262: Human ear
|
||||
263: Human eye
|
||||
264: Human face
|
||||
265: Human foot
|
||||
266: Human hair
|
||||
267: Human hand
|
||||
268: Human head
|
||||
269: Human leg
|
||||
270: Human mouth
|
||||
271: Human nose
|
||||
272: Humidifier
|
||||
273: Ice cream
|
||||
274: Indoor rower
|
||||
275: Infant bed
|
||||
276: Insect
|
||||
277: Invertebrate
|
||||
278: Ipod
|
||||
279: Isopod
|
||||
280: Jacket
|
||||
281: Jacuzzi
|
||||
282: Jaguar (Animal)
|
||||
283: Jeans
|
||||
284: Jellyfish
|
||||
285: Jet ski
|
||||
286: Jug
|
||||
287: Juice
|
||||
288: Kangaroo
|
||||
289: Kettle
|
||||
290: Kitchen & dining room table
|
||||
291: Kitchen appliance
|
||||
292: Kitchen knife
|
||||
293: Kitchen utensil
|
||||
294: Kitchenware
|
||||
295: Kite
|
||||
296: Knife
|
||||
297: Koala
|
||||
298: Ladder
|
||||
299: Ladle
|
||||
300: Ladybug
|
||||
301: Lamp
|
||||
302: Land vehicle
|
||||
303: Lantern
|
||||
304: Laptop
|
||||
305: Lavender (Plant)
|
||||
306: Lemon
|
||||
307: Leopard
|
||||
308: Light bulb
|
||||
309: Light switch
|
||||
310: Lighthouse
|
||||
311: Lily
|
||||
312: Limousine
|
||||
313: Lion
|
||||
314: Lipstick
|
||||
315: Lizard
|
||||
316: Lobster
|
||||
317: Loveseat
|
||||
318: Luggage and bags
|
||||
319: Lynx
|
||||
320: Magpie
|
||||
321: Mammal
|
||||
322: Man
|
||||
323: Mango
|
||||
324: Maple
|
||||
325: Maracas
|
||||
326: Marine invertebrates
|
||||
327: Marine mammal
|
||||
328: Measuring cup
|
||||
329: Mechanical fan
|
||||
330: Medical equipment
|
||||
331: Microphone
|
||||
332: Microwave oven
|
||||
333: Milk
|
||||
334: Miniskirt
|
||||
335: Mirror
|
||||
336: Missile
|
||||
337: Mixer
|
||||
338: Mixing bowl
|
||||
339: Mobile phone
|
||||
340: Monkey
|
||||
341: Moths and butterflies
|
||||
342: Motorcycle
|
||||
343: Mouse
|
||||
344: Muffin
|
||||
345: Mug
|
||||
346: Mule
|
||||
347: Mushroom
|
||||
348: Musical instrument
|
||||
349: Musical keyboard
|
||||
350: Nail (Construction)
|
||||
351: Necklace
|
||||
352: Nightstand
|
||||
353: Oboe
|
||||
354: Office building
|
||||
355: Office supplies
|
||||
356: Orange
|
||||
357: Organ (Musical Instrument)
|
||||
358: Ostrich
|
||||
359: Otter
|
||||
360: Oven
|
||||
361: Owl
|
||||
362: Oyster
|
||||
363: Paddle
|
||||
364: Palm tree
|
||||
365: Pancake
|
||||
366: Panda
|
||||
367: Paper cutter
|
||||
368: Paper towel
|
||||
369: Parachute
|
||||
370: Parking meter
|
||||
371: Parrot
|
||||
372: Pasta
|
||||
373: Pastry
|
||||
374: Peach
|
||||
375: Pear
|
||||
376: Pen
|
||||
377: Pencil case
|
||||
378: Pencil sharpener
|
||||
379: Penguin
|
||||
380: Perfume
|
||||
381: Person
|
||||
382: Personal care
|
||||
383: Personal flotation device
|
||||
384: Piano
|
||||
385: Picnic basket
|
||||
386: Picture frame
|
||||
387: Pig
|
||||
388: Pillow
|
||||
389: Pineapple
|
||||
390: Pitcher (Container)
|
||||
391: Pizza
|
||||
392: Pizza cutter
|
||||
393: Plant
|
||||
394: Plastic bag
|
||||
395: Plate
|
||||
396: Platter
|
||||
397: Plumbing fixture
|
||||
398: Polar bear
|
||||
399: Pomegranate
|
||||
400: Popcorn
|
||||
401: Porch
|
||||
402: Porcupine
|
||||
403: Poster
|
||||
404: Potato
|
||||
405: Power plugs and sockets
|
||||
406: Pressure cooker
|
||||
407: Pretzel
|
||||
408: Printer
|
||||
409: Pumpkin
|
||||
410: Punching bag
|
||||
411: Rabbit
|
||||
412: Raccoon
|
||||
413: Racket
|
||||
414: Radish
|
||||
415: Ratchet (Device)
|
||||
416: Raven
|
||||
417: Rays and skates
|
||||
418: Red panda
|
||||
419: Refrigerator
|
||||
420: Remote control
|
||||
421: Reptile
|
||||
422: Rhinoceros
|
||||
423: Rifle
|
||||
424: Ring binder
|
||||
425: Rocket
|
||||
426: Roller skates
|
||||
427: Rose
|
||||
428: Rugby ball
|
||||
429: Ruler
|
||||
430: Salad
|
||||
431: Salt and pepper shakers
|
||||
432: Sandal
|
||||
433: Sandwich
|
||||
434: Saucer
|
||||
435: Saxophone
|
||||
436: Scale
|
||||
437: Scarf
|
||||
438: Scissors
|
||||
439: Scoreboard
|
||||
440: Scorpion
|
||||
441: Screwdriver
|
||||
442: Sculpture
|
||||
443: Sea lion
|
||||
444: Sea turtle
|
||||
445: Seafood
|
||||
446: Seahorse
|
||||
447: Seat belt
|
||||
448: Segway
|
||||
449: Serving tray
|
||||
450: Sewing machine
|
||||
451: Shark
|
||||
452: Sheep
|
||||
453: Shelf
|
||||
454: Shellfish
|
||||
455: Shirt
|
||||
456: Shorts
|
||||
457: Shotgun
|
||||
458: Shower
|
||||
459: Shrimp
|
||||
460: Sink
|
||||
461: Skateboard
|
||||
462: Ski
|
||||
463: Skirt
|
||||
464: Skull
|
||||
465: Skunk
|
||||
466: Skyscraper
|
||||
467: Slow cooker
|
||||
468: Snack
|
||||
469: Snail
|
||||
470: Snake
|
||||
471: Snowboard
|
||||
472: Snowman
|
||||
473: Snowmobile
|
||||
474: Snowplow
|
||||
475: Soap dispenser
|
||||
476: Sock
|
||||
477: Sofa bed
|
||||
478: Sombrero
|
||||
479: Sparrow
|
||||
480: Spatula
|
||||
481: Spice rack
|
||||
482: Spider
|
||||
483: Spoon
|
||||
484: Sports equipment
|
||||
485: Sports uniform
|
||||
486: Squash (Plant)
|
||||
487: Squid
|
||||
488: Squirrel
|
||||
489: Stairs
|
||||
490: Stapler
|
||||
491: Starfish
|
||||
492: Stationary bicycle
|
||||
493: Stethoscope
|
||||
494: Stool
|
||||
495: Stop sign
|
||||
496: Strawberry
|
||||
497: Street light
|
||||
498: Stretcher
|
||||
499: Studio couch
|
||||
500: Submarine
|
||||
501: Submarine sandwich
|
||||
502: Suit
|
||||
503: Suitcase
|
||||
504: Sun hat
|
||||
505: Sunglasses
|
||||
506: Surfboard
|
||||
507: Sushi
|
||||
508: Swan
|
||||
509: Swim cap
|
||||
510: Swimming pool
|
||||
511: Swimwear
|
||||
512: Sword
|
||||
513: Syringe
|
||||
514: Table
|
||||
515: Table tennis racket
|
||||
516: Tablet computer
|
||||
517: Tableware
|
||||
518: Taco
|
||||
519: Tank
|
||||
520: Tap
|
||||
521: Tart
|
||||
522: Taxi
|
||||
523: Tea
|
||||
524: Teapot
|
||||
525: Teddy bear
|
||||
526: Telephone
|
||||
527: Television
|
||||
528: Tennis ball
|
||||
529: Tennis racket
|
||||
530: Tent
|
||||
531: Tiara
|
||||
532: Tick
|
||||
533: Tie
|
||||
534: Tiger
|
||||
535: Tin can
|
||||
536: Tire
|
||||
537: Toaster
|
||||
538: Toilet
|
||||
539: Toilet paper
|
||||
540: Tomato
|
||||
541: Tool
|
||||
542: Toothbrush
|
||||
543: Torch
|
||||
544: Tortoise
|
||||
545: Towel
|
||||
546: Tower
|
||||
547: Toy
|
||||
548: Traffic light
|
||||
549: Traffic sign
|
||||
550: Train
|
||||
551: Training bench
|
||||
552: Treadmill
|
||||
553: Tree
|
||||
554: Tree house
|
||||
555: Tripod
|
||||
556: Trombone
|
||||
557: Trousers
|
||||
558: Truck
|
||||
559: Trumpet
|
||||
560: Turkey
|
||||
561: Turtle
|
||||
562: Umbrella
|
||||
563: Unicycle
|
||||
564: Van
|
||||
565: Vase
|
||||
566: Vegetable
|
||||
567: Vehicle
|
||||
568: Vehicle registration plate
|
||||
569: Violin
|
||||
570: Volleyball (Ball)
|
||||
571: Waffle
|
||||
572: Waffle iron
|
||||
573: Wall clock
|
||||
574: Wardrobe
|
||||
575: Washing machine
|
||||
576: Waste container
|
||||
577: Watch
|
||||
578: Watercraft
|
||||
579: Watermelon
|
||||
580: Weapon
|
||||
581: Whale
|
||||
582: Wheel
|
||||
583: Wheelchair
|
||||
584: Whisk
|
||||
585: Whiteboard
|
||||
586: Willow
|
||||
587: Window
|
||||
588: Window blind
|
||||
589: Wine
|
||||
590: Wine glass
|
||||
591: Wine rack
|
||||
592: Winter melon
|
||||
593: Wok
|
||||
594: Woman
|
||||
595: Wood-burning stove
|
||||
596: Woodpecker
|
||||
597: Worm
|
||||
598: Wrench
|
||||
599: Zebra
|
||||
600: Zucchini
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
|
||||
from ultralytics.utils.checks import check_requirements, check_version
|
||||
|
||||
check_requirements('fiftyone')
|
||||
if is_ubuntu() and check_version(get_ubuntu_version(), '>=22.04'):
|
||||
# Ubuntu>=22.04 patch https://github.com/voxel51/fiftyone/issues/2961#issuecomment-1666519347
|
||||
check_requirements('fiftyone-db-ubuntu2204')
|
||||
|
||||
import fiftyone as fo
|
||||
import fiftyone.zoo as foz
|
||||
import warnings
|
||||
|
||||
name = 'open-images-v7'
|
||||
fraction = 1.0 # fraction of full dataset to use
|
||||
LOGGER.warning('WARNING ⚠️ Open Images V7 dataset requires at least **561 GB of free space. Starting download...')
|
||||
for split in 'train', 'validation': # 1743042 train, 41620 val images
|
||||
train = split == 'train'
|
||||
|
||||
# Load Open Images dataset
|
||||
dataset = foz.load_zoo_dataset(name,
|
||||
split=split,
|
||||
label_types=['detections'],
|
||||
dataset_dir=Path(SETTINGS['datasets_dir']) / 'fiftyone' / name,
|
||||
max_samples=round((1743042 if train else 41620) * fraction))
|
||||
|
||||
# Define classes
|
||||
if train:
|
||||
classes = dataset.default_classes # all classes
|
||||
# classes = dataset.distinct('ground_truth.detections.label') # only observed classes
|
||||
|
||||
# Export to YOLO format
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
|
||||
dataset.export(export_dir=str(Path(SETTINGS['datasets_dir']) / name),
|
||||
dataset_type=fo.types.YOLOv5Dataset,
|
||||
label_field='ground_truth',
|
||||
split='val' if split == 'validation' else split,
|
||||
classes=classes,
|
||||
overwrite=train)
|
||||
@ -1,21 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Package-seg dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
|
||||
# Example usage: yolo train data=package-seg.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── package-seg ← downloads here (102 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/package-seg # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1920 images
|
||||
val: images/val # val images (relative to 'path') 89 images
|
||||
test: test/images # test images (relative to 'path') 188 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: package
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/package-seg.zip
|
||||
@ -1,24 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Tiger Pose dataset by Ultralytics
|
||||
# Documentation: https://docs.ultralytics.com/datasets/pose/tiger-pose/
|
||||
# Example usage: yolo train data=tiger-pose.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── tiger-pose ← downloads here (75.3 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/tiger-pose # dataset root dir
|
||||
train: train # train images (relative to 'path') 210 images
|
||||
val: val # val images (relative to 'path') 53 images
|
||||
|
||||
# Keypoints
|
||||
kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: tiger
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://ultralytics.com/assets/tiger-pose.zip
|
||||
@ -1,152 +0,0 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
|
||||
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
|
||||
# Documentation: https://docs.ultralytics.com/datasets/detect/xview/
|
||||
# Example usage: yolo train data=xView.yaml
|
||||
# parent
|
||||
# ├── ultralytics
|
||||
# └── datasets
|
||||
# └── xView ← downloads here (20.7 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/xView # dataset root dir
|
||||
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Fixed-wing Aircraft
|
||||
1: Small Aircraft
|
||||
2: Cargo Plane
|
||||
3: Helicopter
|
||||
4: Passenger Vehicle
|
||||
5: Small Car
|
||||
6: Bus
|
||||
7: Pickup Truck
|
||||
8: Utility Truck
|
||||
9: Truck
|
||||
10: Cargo Truck
|
||||
11: Truck w/Box
|
||||
12: Truck Tractor
|
||||
13: Trailer
|
||||
14: Truck w/Flatbed
|
||||
15: Truck w/Liquid
|
||||
16: Crane Truck
|
||||
17: Railway Vehicle
|
||||
18: Passenger Car
|
||||
19: Cargo Car
|
||||
20: Flat Car
|
||||
21: Tank car
|
||||
22: Locomotive
|
||||
23: Maritime Vessel
|
||||
24: Motorboat
|
||||
25: Sailboat
|
||||
26: Tugboat
|
||||
27: Barge
|
||||
28: Fishing Vessel
|
||||
29: Ferry
|
||||
30: Yacht
|
||||
31: Container Ship
|
||||
32: Oil Tanker
|
||||
33: Engineering Vehicle
|
||||
34: Tower crane
|
||||
35: Container Crane
|
||||
36: Reach Stacker
|
||||
37: Straddle Carrier
|
||||
38: Mobile Crane
|
||||
39: Dump Truck
|
||||
40: Haul Truck
|
||||
41: Scraper/Tractor
|
||||
42: Front loader/Bulldozer
|
||||
43: Excavator
|
||||
44: Cement Mixer
|
||||
45: Ground Grader
|
||||
46: Hut/Tent
|
||||
47: Shed
|
||||
48: Building
|
||||
49: Aircraft Hangar
|
||||
50: Damaged Building
|
||||
51: Facility
|
||||
52: Construction Site
|
||||
53: Vehicle Lot
|
||||
54: Helipad
|
||||
55: Storage Tank
|
||||
56: Shipping container lot
|
||||
57: Shipping Container
|
||||
58: Pylon
|
||||
59: Tower
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.data.utils import autosplit
|
||||
from ultralytics.utils.ops import xyxy2xywhn
|
||||
|
||||
|
||||
def convert_labels(fname=Path('xView/xView_train.geojson')):
|
||||
# Convert xView geoJSON labels to YOLO format
|
||||
path = fname.parent
|
||||
with open(fname) as f:
|
||||
print(f'Loading {fname}...')
|
||||
data = json.load(f)
|
||||
|
||||
# Make dirs
|
||||
labels = Path(path / 'labels' / 'train')
|
||||
os.system(f'rm -rf {labels}')
|
||||
labels.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# xView classes 11-94 to 0-59
|
||||
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
|
||||
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
|
||||
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
|
||||
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
|
||||
|
||||
shapes = {}
|
||||
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
|
||||
p = feature['properties']
|
||||
if p['bounds_imcoords']:
|
||||
id = p['image_id']
|
||||
file = path / 'train_images' / id
|
||||
if file.exists(): # 1395.tif missing
|
||||
try:
|
||||
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
|
||||
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
|
||||
cls = p['type_id']
|
||||
cls = xview_class2index[int(cls)] # xView class to 0-60
|
||||
assert 59 >= cls >= 0, f'incorrect class index {cls}'
|
||||
|
||||
# Write YOLO label
|
||||
if id not in shapes:
|
||||
shapes[id] = Image.open(file).size
|
||||
box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
|
||||
with open((labels / id).with_suffix('.txt'), 'a') as f:
|
||||
f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
|
||||
except Exception as e:
|
||||
print(f'WARNING: skipping one label for {file}: {e}')
|
||||
|
||||
|
||||
# Download manually from https://challenge.xviewdataset.org
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
# urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
|
||||
# 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
|
||||
# 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
|
||||
# download(urls, dir=dir)
|
||||
|
||||
# Convert labels
|
||||
convert_labels(dir / 'xView_train.geojson')
|
||||
|
||||
# Move images
|
||||
images = Path(dir / 'images')
|
||||
images.mkdir(parents=True, exist_ok=True)
|
||||
Path(dir / 'train_images').rename(dir / 'images' / 'train')
|
||||
Path(dir / 'val_images').rename(dir / 'images' / 'val')
|
||||
|
||||
# Split
|
||||
autosplit(dir / 'images' / 'train')
|
||||
@ -10,14 +10,14 @@ data: # (str, optional) path to data file, i.e. coco128.yaml
|
||||
epochs: 100 # (int) number of epochs to train for
|
||||
time: # (float, optional) number of hours to train for, overrides epochs if supplied
|
||||
patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
|
||||
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
|
||||
batch: 8 # (int) number of images per batch (-1 for AutoBatch)
|
||||
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
|
||||
save: True # (bool) save train checkpoints and predict results
|
||||
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
|
||||
val_period: 1 # (int) Validation every x epochs
|
||||
cache: False # (bool) True/ram, disk or False. Use cache for data loading
|
||||
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
|
||||
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
|
||||
workers: 2 # (int) number of worker threads for data loading (per RANK if DDP)
|
||||
project: # (str, optional) project name
|
||||
name: # (str, optional) experiment name, results saved to 'project/name' directory
|
||||
exist_ok: False # (bool) whether to overwrite existing experiment
|
||||
|
||||
40
ultralytics/cfg/models/v10/yolov10b-seg.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10b-seg.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
b: [0.67, 1.00, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Segment, [nc, 32, 256]] # segment(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10l-seg.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10l-seg.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Segment, [nc, 32, 256]] # segment(P3, P4, P5)
|
||||
43
ultralytics/cfg/models/v10/yolov10m-seg.yaml
Normal file
43
ultralytics/cfg/models/v10/yolov10m-seg.yaml
Normal file
@ -0,0 +1,43 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Segment, [nc, 32, 256]] # segment(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10n-seg.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10n-seg.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Segment, [nc, 32, 256]] # segment(P3, P4, P5)
|
||||
39
ultralytics/cfg/models/v10/yolov10s-seg.yaml
Normal file
39
ultralytics/cfg/models/v10/yolov10s-seg.yaml
Normal file
@ -0,0 +1,39 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
s: [0.33, 0.50, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Segment, [nc, 32, 256]] # segment(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10x-seg.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10x-seg.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2fCIB, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Segment, [nc, 32, 256]] # segment(P3, P4, P5)
|
||||
@ -267,7 +267,7 @@ class BaseDataset(Dataset):
|
||||
|
||||
def __len__(self):
|
||||
"""Returns the length of the labels list for the dataset."""
|
||||
return len(self.labels)
|
||||
return int(0.1*len(self.labels))
|
||||
|
||||
def update_labels_info(self, label):
|
||||
"""Custom your label format here."""
|
||||
|
||||
@ -268,7 +268,7 @@ class BaseTrainer:
|
||||
self.amp = torch.tensor(self.args.amp).to(self.device) # True or False
|
||||
if self.amp and RANK in (-1, 0): # Single-GPU and DDP
|
||||
callbacks_backup = callbacks.default_callbacks.copy() # backup callbacks as check_amp() resets them
|
||||
self.amp = torch.tensor(check_amp(self.model), device=self.device)
|
||||
self.amp = True# torch.tensor(check_amp(self.model), device=self.device)
|
||||
callbacks.default_callbacks = callbacks_backup # restore callbacks
|
||||
if RANK > -1 and world_size > 1: # DDP
|
||||
dist.broadcast(self.amp, src=0) # broadcast the tensor from rank 0 to all other ranks (returns None)
|
||||
@ -420,6 +420,7 @@ class BaseTrainer:
|
||||
|
||||
self.lr = {f"lr/pg{ir}": x["lr"] for ir, x in enumerate(self.optimizer.param_groups)} # for loggers
|
||||
self.run_callbacks("on_train_epoch_end")
|
||||
# validate
|
||||
if RANK in (-1, 0):
|
||||
final_epoch = epoch + 1 == self.epochs
|
||||
self.ema.update_attr(self.model, include=["yaml", "nc", "args", "names", "stride", "class_weights"])
|
||||
|
||||
@ -187,7 +187,7 @@ class BaseValidator:
|
||||
preds = self.postprocess(preds)
|
||||
|
||||
self.update_metrics(preds, batch)
|
||||
if self.args.plots and batch_i < 3:
|
||||
if False:#self.args.plots and batch_i < 3:
|
||||
self.plot_val_samples(batch, batch_i)
|
||||
self.plot_predictions(batch, preds, batch_i)
|
||||
|
||||
|
||||
@ -4,5 +4,5 @@ from .rtdetr import RTDETR
|
||||
from .sam import SAM
|
||||
from .yolo import YOLO, YOLOWorld
|
||||
from .yolov10 import YOLOv10
|
||||
|
||||
__all__ = "YOLO", "RTDETR", "SAM", "YOLOWorld", "YOLOv10" # allow simpler import
|
||||
from .yolov10seg import YOLOv10Seg
|
||||
__all__ = "YOLO", "RTDETR", "SAM", "YOLOWorld", "YOLOv10","YOLOv10Seg" # allow simpler import
|
||||
|
||||
@ -7,7 +7,7 @@ from .train import YOLOv10DetectionTrainer
|
||||
from huggingface_hub import PyTorchModelHubMixin
|
||||
from .card import card_template_text
|
||||
|
||||
class YOLOv10(Model, PyTorchModelHubMixin, model_card_template=card_template_text):
|
||||
class YOLOv10(Model):# , PyTorchModelHubMixin, model_card_template=card_template_text):
|
||||
|
||||
def __init__(self, model="yolov10n.pt", task=None, verbose=False,
|
||||
names=None):
|
||||
@ -33,4 +33,6 @@ class YOLOv10(Model, PyTorchModelHubMixin, model_card_template=card_template_tex
|
||||
"validator": YOLOv10DetectionValidator,
|
||||
"predictor": YOLOv10DetectionPredictor,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
5
ultralytics/models/yolov10seg/__init__.py
Normal file
5
ultralytics/models/yolov10seg/__init__.py
Normal file
@ -0,0 +1,5 @@
|
||||
from .model import YOLOv10Seg
|
||||
from .predict import YOLOv10SegPredictor
|
||||
from .val import YOLOv10SegValidator
|
||||
|
||||
__all__ = "YOLOv10DetectionPredictor", "YOLOv10DetectionValidator", "YOLOv10Seg"
|
||||
64
ultralytics/models/yolov10seg/card.py
Normal file
64
ultralytics/models/yolov10seg/card.py
Normal file
@ -0,0 +1,64 @@
|
||||
card_template_text = """
|
||||
---
|
||||
license: agpl-3.0
|
||||
library_name: ultralytics
|
||||
repo_url: https://github.com/THU-MIG/yolov10
|
||||
tags:
|
||||
- object-detection
|
||||
- computer-vision
|
||||
- yolov10
|
||||
datasets:
|
||||
- detection-datasets/coco
|
||||
inference: false
|
||||
---
|
||||
|
||||
### Model Description
|
||||
[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458v1)
|
||||
|
||||
- arXiv: https://arxiv.org/abs/2405.14458v1
|
||||
- github: https://github.com/THU-MIG/yolov10
|
||||
|
||||
### Installation
|
||||
```
|
||||
pip install git+https://github.com/THU-MIG/yolov10.git
|
||||
```
|
||||
|
||||
### Training and validation
|
||||
```python
|
||||
from ultralytics import YOLOv10
|
||||
|
||||
model = YOLOv10.from_pretrained('jameslahm/yolov10n')
|
||||
# Training
|
||||
model.train(...)
|
||||
# after training, one can push to the hub
|
||||
model.push_to_hub("your-hf-username/yolov10-finetuned")
|
||||
|
||||
# Validation
|
||||
model.val(...)
|
||||
```
|
||||
|
||||
### Inference
|
||||
|
||||
Here's an end-to-end example showcasing inference on a cats image:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLOv10
|
||||
|
||||
model = YOLOv10.from_pretrained('jameslahm/yolov10n')
|
||||
source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
|
||||
model.predict(source=source, save=True)
|
||||
```
|
||||
which shows:
|
||||
|
||||
|
||||
|
||||
### BibTeX Entry and Citation Info
|
||||
```
|
||||
@article{wang2024yolov10,
|
||||
title={YOLOv10: Real-Time End-to-End Object Detection},
|
||||
author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
|
||||
journal={arXiv preprint arXiv:2405.14458},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
""".strip()
|
||||
36
ultralytics/models/yolov10seg/model.py
Normal file
36
ultralytics/models/yolov10seg/model.py
Normal file
@ -0,0 +1,36 @@
|
||||
from ultralytics.engine.model import Model
|
||||
from ultralytics.nn.tasks import YOLOv10SegmentationModel
|
||||
from .val import YOLOv10SegValidator
|
||||
from .predict import YOLOv10SegPredictor
|
||||
from .train import YOLOv10SegTrainer
|
||||
|
||||
from huggingface_hub import PyTorchModelHubMixin
|
||||
from .card import card_template_text
|
||||
|
||||
class YOLOv10Seg(Model):# , PyTorchModelHubMixin, model_card_template=card_template_text):
|
||||
|
||||
def __init__(self, model="yolov10n.pt", task=None, verbose=False,
|
||||
names=None):
|
||||
super().__init__(model=model, task=task, verbose=verbose)
|
||||
if names is not None:
|
||||
setattr(self.model, 'names', names)
|
||||
|
||||
def push_to_hub(self, repo_name, **kwargs):
|
||||
config = kwargs.get('config', {})
|
||||
config['names'] = self.names
|
||||
config['model'] = self.model.yaml['yaml_file']
|
||||
config['task'] = self.task
|
||||
kwargs['config'] = config
|
||||
super().push_to_hub(repo_name, **kwargs)
|
||||
|
||||
@property
|
||||
def task_map(self):
|
||||
"""Map head to model, trainer, validator, and predictor classes."""
|
||||
return {
|
||||
"segment": {
|
||||
"model": YOLOv10SegmentationModel,
|
||||
"trainer": YOLOv10SegTrainer,
|
||||
"validator": YOLOv10SegValidator,
|
||||
"predictor": YOLOv10SegPredictor,
|
||||
},
|
||||
}
|
||||
45
ultralytics/models/yolov10seg/predict.py
Normal file
45
ultralytics/models/yolov10seg/predict.py
Normal file
@ -0,0 +1,45 @@
|
||||
from ultralytics.models.yolo.detect import DetectionPredictor
|
||||
import torch
|
||||
from ultralytics.utils import ops
|
||||
from ultralytics.engine.results import Results
|
||||
import torch.nn.functional as F
|
||||
|
||||
class YOLOv10SegPredictor(DetectionPredictor):
|
||||
def postprocess(self, preds, img, orig_imgs):
|
||||
coef,proto = None,None
|
||||
if isinstance(preds, dict):
|
||||
coef = preds['coef']
|
||||
proto = preds['proto']
|
||||
preds = preds["one2one"]
|
||||
if isinstance(preds, (list, tuple)):
|
||||
preds = preds[0] # [1,5,6006] coef[1,32,6006] proto[1,32,104,176]
|
||||
|
||||
if preds.shape[-1] == 6:
|
||||
pass
|
||||
else:
|
||||
preds = preds.transpose(-1, -2) # [1,6006,5]
|
||||
coef = coef.transpose(-1, -2)
|
||||
# bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4)
|
||||
bboxes, scores, labels,segmask = ops.v10segpostprocess([preds,coef,proto], self.args.max_det, preds.shape[-1]-4)
|
||||
bboxes = ops.xywh2xyxy(bboxes)
|
||||
preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
|
||||
mask = preds[..., 4] > self.args.conf
|
||||
if self.args.classes is not None:
|
||||
mask = mask & (preds[..., 5:6] == torch.tensor(self.args.classes, device=preds.device).unsqueeze(0)).any(2)
|
||||
|
||||
preds = [p[mask[idx]] for idx, p in enumerate(preds)]
|
||||
segmask = [p[mask[idx]] for idx, p in enumerate(segmask)]
|
||||
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
|
||||
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
|
||||
|
||||
results = []
|
||||
for i, pred in enumerate(preds):
|
||||
orig_img = orig_imgs[i]
|
||||
seg = segmask[i]
|
||||
cc,hh,ww = seg.shape
|
||||
seg = F.interpolate(seg[None], (hh*4, ww*4), mode="bilinear", align_corners=False)[0].gt_(0)
|
||||
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
|
||||
|
||||
img_path = self.batch[0][i]
|
||||
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred,masks=seg))
|
||||
return results
|
||||
20
ultralytics/models/yolov10seg/train.py
Normal file
20
ultralytics/models/yolov10seg/train.py
Normal file
@ -0,0 +1,20 @@
|
||||
from ultralytics.models.yolo.detect import DetectionTrainer
|
||||
from .val import YOLOv10SegValidator
|
||||
from .model import YOLOv10SegmentationModel
|
||||
from copy import copy
|
||||
from ultralytics.utils import RANK
|
||||
|
||||
class YOLOv10SegTrainer(DetectionTrainer):
|
||||
def get_validator(self):
|
||||
"""Returns a DetectionValidator for YOLO model validation."""
|
||||
self.loss_names = "box_om", "cls_om", "dfl_om", "box_oo", "cls_oo", "dfl_oo",
|
||||
return YOLOv10SegValidator(
|
||||
self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
|
||||
)
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
"""Return a YOLO detection model."""
|
||||
model = YOLOv10SegmentationModel(cfg, nc=self.data["nc"], verbose=verbose and RANK == -1)
|
||||
if weights:
|
||||
model.load(weights)
|
||||
return model
|
||||
226
ultralytics/models/yolov10seg/val.py
Normal file
226
ultralytics/models/yolov10seg/val.py
Normal file
@ -0,0 +1,226 @@
|
||||
from ultralytics.models.yolo.detect import DetectionValidator
|
||||
from ultralytics.utils import ops
|
||||
import torch
|
||||
from ultralytics.utils.metrics import SegmentMetrics, box_iou, mask_iou
|
||||
import torch.nn.functional as F
|
||||
class YOLOv10SegValidator(DetectionValidator):
|
||||
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
|
||||
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
|
||||
self.args.save_json |= self.is_coco
|
||||
self.plot_masks = None
|
||||
self.process = None
|
||||
self.args.task = "segment"
|
||||
self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
|
||||
def init_metrics(self, model):
|
||||
"""Initialize metrics and select mask processing function based on save_json flag."""
|
||||
super().init_metrics(model)
|
||||
self.plot_masks = []
|
||||
if self.args.save_json:
|
||||
# check_requirements("pycocotools>=2.0.6")
|
||||
self.process = ops.process_mask_upsample # more accurate
|
||||
else:
|
||||
self.process = ops.process_mask # faster
|
||||
self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[])
|
||||
def get_desc(self):
|
||||
"""Return a formatted description of evaluation metrics."""
|
||||
return ("%22s" + "%11s" * 10) % (
|
||||
"Class",
|
||||
"Images",
|
||||
"Instances",
|
||||
"Box(P",
|
||||
"R",
|
||||
"mAP50",
|
||||
"mAP50-95)",
|
||||
"Mask(P",
|
||||
"R",
|
||||
"mAP50",
|
||||
"mAP50-95)",
|
||||
)
|
||||
def _prepare_batch(self, si, batch):
|
||||
"""Prepares a batch for training or inference by processing images and targets."""
|
||||
prepared_batch = super()._prepare_batch(si, batch)
|
||||
midx = [si] if self.args.overlap_mask else batch["batch_idx"] == si
|
||||
prepared_batch["masks"] = batch["masks"][midx]
|
||||
return prepared_batch
|
||||
|
||||
def finalize_metrics(self, *args, **kwargs):
|
||||
"""Sets speed and confusion matrix for evaluation metrics."""
|
||||
self.metrics.speed = self.speed
|
||||
self.metrics.confusion_matrix = self.confusion_matrix
|
||||
def preprocess(self, batch):
|
||||
"""Preprocesses batch by converting masks to float and sending to device."""
|
||||
batch = super().preprocess(batch)
|
||||
batch["masks"] = batch["masks"].to(self.device).float()
|
||||
return batch
|
||||
def postprocess(self, preds):
|
||||
coef,proto = None,None
|
||||
if isinstance(preds, dict):
|
||||
coef = preds["coef"] # [1,32,5294]
|
||||
proto = preds["proto"] # [1,32,92,168]
|
||||
preds = preds["one2one"]
|
||||
|
||||
if isinstance(preds, (list, tuple)):
|
||||
preds = preds[0]
|
||||
|
||||
# Acknowledgement: Thanks to sanha9999 in #190 and #181!
|
||||
if preds.shape[-1] == 6:
|
||||
return preds
|
||||
else:
|
||||
preds = preds.transpose(-1, -2) # [1,6006,5]
|
||||
coef = coef.transpose(-1, -2)
|
||||
# bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4)
|
||||
bboxes, scores, labels,segmask = ops.v10segpostprocess([preds,coef,proto], self.args.max_det, preds.shape[-1]-4)
|
||||
bboxes = ops.xywh2xyxy(bboxes)
|
||||
preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
|
||||
return [preds,segmask]
|
||||
def process_mask(self,segmask, bboxes, shape, upsample=False):
|
||||
c, mh, mw = segmask.shape # CHW
|
||||
ih, iw = shape
|
||||
width_ratio = mw / iw
|
||||
height_ratio = mh / ih
|
||||
|
||||
downsampled_bboxes = bboxes.clone()
|
||||
downsampled_bboxes[:, 0] *= width_ratio
|
||||
downsampled_bboxes[:, 2] *= width_ratio
|
||||
downsampled_bboxes[:, 3] *= height_ratio
|
||||
downsampled_bboxes[:, 1] *= height_ratio
|
||||
|
||||
segmask = ops.crop_mask(segmask, downsampled_bboxes) # CHW
|
||||
if upsample:
|
||||
segmask = F.interpolate(segmask[None], shape, mode="bilinear", align_corners=False)[0] # CHW
|
||||
return segmask.gt_(0)
|
||||
|
||||
def _prepare_pred(self, inputx, pbatch):
|
||||
"""Prepares a batch for training or inference by processing images and targets."""
|
||||
pred, pred_masks = None,None
|
||||
if isinstance(inputx,list):
|
||||
pred, pred_masks = inputx
|
||||
else:
|
||||
print("error!!!!!!!")
|
||||
predn = pred.clone()
|
||||
predn[:, :4] = ops.scale_boxes(
|
||||
pbatch["imgsz"], predn[:, :4], pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]
|
||||
) # native-space pred
|
||||
pred_masks = self.process_mask(pred_masks, pred[:, :4], pbatch["imgsz"])
|
||||
return predn, pred_masks
|
||||
|
||||
def _process_batch(self, detections, gt_bboxes, gt_cls, pred_masks=None, gt_masks=None, overlap=False, masks=False):
|
||||
"""
|
||||
Return correct prediction matrix.
|
||||
|
||||
Args:
|
||||
detections (array[N, 6]), x1, y1, x2, y2, conf, class
|
||||
labels (array[M, 5]), class, x1, y1, x2, y2
|
||||
|
||||
Returns:
|
||||
correct (array[N, 10]), for 10 IoU levels
|
||||
"""
|
||||
if masks:
|
||||
if overlap:
|
||||
nl = len(gt_cls)
|
||||
index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
|
||||
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
|
||||
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
|
||||
if gt_masks.shape[1:] != pred_masks.shape[1:]:
|
||||
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
|
||||
gt_masks = gt_masks.gt_(0.5)
|
||||
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
|
||||
else: # boxes
|
||||
iou = box_iou(gt_bboxes, detections[:, :4])
|
||||
|
||||
return self.match_predictions(detections[:, 5], gt_cls, iou)
|
||||
def update_metrics(self, preds, batch):
|
||||
"""Metrics."""
|
||||
# box + score + label
|
||||
# preds [[1,300,6],[1,300,96,168]]
|
||||
for si, (pred, pred_masks) in enumerate(zip(preds[0], preds[1])):
|
||||
self.seen += 1
|
||||
npr = len(pred) # 300
|
||||
stat = dict(
|
||||
conf=torch.zeros(0, device=self.device),
|
||||
pred_cls=torch.zeros(0, device=self.device),
|
||||
tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
|
||||
tp_m=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
|
||||
)
|
||||
pbatch = self._prepare_batch(si, batch)
|
||||
cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
|
||||
nl = len(cls)
|
||||
stat["target_cls"] = cls
|
||||
if npr == 0:
|
||||
if nl:
|
||||
for k in self.stats.keys():
|
||||
self.stats[k].append(stat[k])
|
||||
if self.args.plots:
|
||||
self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
|
||||
continue
|
||||
|
||||
# Masks
|
||||
gt_masks = pbatch.pop("masks")
|
||||
# Predictions
|
||||
if self.args.single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn,pred_masks = self._prepare_pred([pred,pred_masks], pbatch)
|
||||
|
||||
stat["conf"] = predn[:, 4]
|
||||
stat["pred_cls"] = predn[:, 5]
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
stat["tp"] = self._process_batch(predn, bbox, cls)
|
||||
stat["tp_m"] = self._process_batch(
|
||||
predn, bbox, cls, pred_masks, gt_masks, self.args.overlap_mask, masks=True
|
||||
)
|
||||
if self.args.plots:
|
||||
self.confusion_matrix.process_batch(predn, bbox, cls)
|
||||
|
||||
for k in self.stats.keys():
|
||||
self.stats[k].append(stat[k])
|
||||
|
||||
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
|
||||
if self.args.plots and self.batch_i < 3:
|
||||
self.plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
|
||||
|
||||
# Save
|
||||
if False:# self.args.save_json:
|
||||
pred_masks = ops.scale_image(
|
||||
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
|
||||
pbatch["ori_shape"],
|
||||
ratio_pad=batch["ratio_pad"][si],
|
||||
)
|
||||
self.pred_to_json(predn, batch["im_file"][si], pred_masks)
|
||||
|
||||
"""class YOLOv10SegValidator(DetectionValidator):
|
||||
|
||||
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
|
||||
if overrides is None:
|
||||
overrides = {}
|
||||
overrides["task"] = "segment"
|
||||
super().__init__(cfg, overrides, _callbacks)
|
||||
|
||||
def get_model(self, cfg=None, weights=None, verbose=True):
|
||||
model = SegmentationModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose and RANK == -1)
|
||||
if weights:
|
||||
model.load(weights)
|
||||
|
||||
return model
|
||||
|
||||
def get_validator(self):
|
||||
self.loss_names = "box_loss", "seg_loss", "cls_loss", "dfl_loss"
|
||||
return yolo.segment.SegmentationValidator(
|
||||
self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
|
||||
)
|
||||
|
||||
def plot_training_samples(self, batch, ni):
|
||||
plot_images(
|
||||
batch["img"],
|
||||
batch["batch_idx"],
|
||||
batch["cls"].squeeze(-1),
|
||||
batch["bboxes"],
|
||||
masks=batch["masks"],
|
||||
paths=batch["im_file"],
|
||||
fname=self.save_dir / f"train_batch{ni}.jpg",
|
||||
on_plot=self.on_plot,
|
||||
)
|
||||
|
||||
def plot_metrics(self):
|
||||
plot_results(file=self.csv, segment=True, on_plot=self.on_plot) # save results.png"""
|
||||
@ -66,7 +66,7 @@ from .conv import (
|
||||
RepConv,
|
||||
SpatialAttention,
|
||||
)
|
||||
from .head import OBB, Classify, Detect, Pose, RTDETRDecoder, Segment, WorldDetect, v10Detect
|
||||
from .head import OBB, Classify, Detect, Pose, RTDETRDecoder, Segment, WorldDetect, v10Detect,v10Segment
|
||||
from .transformer import (
|
||||
AIFI,
|
||||
MLP,
|
||||
@ -143,5 +143,6 @@ __all__ = (
|
||||
"C2fCIB",
|
||||
"SCDown",
|
||||
"RepVGGDW",
|
||||
"v10Detect"
|
||||
"v10Detect",
|
||||
"v10Segment"
|
||||
)
|
||||
|
||||
@ -494,8 +494,8 @@ class RTDETRDecoder(nn.Module):
|
||||
for layer in self.input_proj:
|
||||
xavier_uniform_(layer[0].weight)
|
||||
|
||||
class v10Detect(Detect):
|
||||
|
||||
class v10Detect(Detect):
|
||||
max_det = 300
|
||||
|
||||
def __init__(self, nc=80, ch=()):
|
||||
@ -515,13 +515,13 @@ class v10Detect(Detect):
|
||||
|
||||
if not self.training:
|
||||
one2one = self.inference(one2one)
|
||||
if not self.export:
|
||||
if not self.export:
|
||||
return {"one2many": one2many, "one2one": one2one}
|
||||
else:
|
||||
else:
|
||||
assert(self.max_det != -1)
|
||||
boxes, scores, labels = ops.v10postprocess(one2one.permute(0, 2, 1), self.max_det, self.nc)
|
||||
return torch.cat([boxes, scores.unsqueeze(-1), labels.unsqueeze(-1).to(boxes.dtype)], dim=-1)
|
||||
else:
|
||||
else:
|
||||
return {"one2many": one2many, "one2one": one2one}
|
||||
|
||||
def bias_init(self):
|
||||
@ -533,3 +533,39 @@ class v10Detect(Detect):
|
||||
for a, b, s in zip(m.one2one_cv2, m.one2one_cv3, m.stride): # from
|
||||
a[-1].bias.data[:] = 1.0 # box
|
||||
b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
|
||||
|
||||
class v10Segment(v10Detect):
|
||||
|
||||
def __init__(self, nc=80,nm=32, npr=256,ch=()):
|
||||
super().__init__(nc, ch)
|
||||
# seg
|
||||
self.nm = nm
|
||||
self.npr = npr
|
||||
self.proto = Proto(ch[0], self.npr, self.nm) # protos
|
||||
self.detect = v10Detect.forward
|
||||
c4 = max(ch[0] // 4, self.nm)
|
||||
self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
p = self.proto(x[0]) # mask protos
|
||||
bs = p.shape[0] # batch size
|
||||
# mask coefficients
|
||||
mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)
|
||||
x = self.detect(self, x)
|
||||
# if self.training:
|
||||
# return x, mc, p
|
||||
# return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))
|
||||
|
||||
if not self.training:
|
||||
if not self.export: # 验证
|
||||
x["coef"] = mc
|
||||
x["proto"] = p
|
||||
return x
|
||||
# return {"one2many": one2many, "one2one": one2one,"coef":mc, "proto":p}
|
||||
else: # 导出onnx
|
||||
return x,mc,p
|
||||
else: # 训练
|
||||
x["coef"] = mc
|
||||
x["proto"] = p
|
||||
return x # {"one2many": one2many, "one2one": one2one,"coef":mc, "proto":p}
|
||||
@ -53,11 +53,12 @@ from ultralytics.nn.modules import (
|
||||
PSA,
|
||||
SCDown,
|
||||
RepVGGDW,
|
||||
v10Detect
|
||||
v10Detect,
|
||||
v10Segment
|
||||
)
|
||||
from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
|
||||
from ultralytics.utils.checks import check_requirements, check_suffix, check_yaml
|
||||
from ultralytics.utils.loss import v8ClassificationLoss, v8DetectionLoss, v8OBBLoss, v8PoseLoss, v8SegmentationLoss, v10DetectLoss
|
||||
from ultralytics.utils.loss import v8ClassificationLoss, v8DetectionLoss, v8OBBLoss, v8PoseLoss, v8SegmentationLoss, v10DetectLoss,v10SegmentationLoss
|
||||
from ultralytics.utils.plotting import feature_visualization
|
||||
from ultralytics.utils.torch_utils import (
|
||||
fuse_conv_and_bn,
|
||||
@ -303,7 +304,9 @@ class DetectionModel(BaseModel):
|
||||
m.inplace = self.inplace
|
||||
forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
|
||||
if isinstance(m, v10Detect):
|
||||
forward = lambda x: self.forward(x)["one2many"]
|
||||
forward = lambda x: self.forward(x)["one2many"] if isinstance(m, (v10Segment)) else self.forward(x)["one2many"]
|
||||
# forward = lambda x: self.forward(x)["one2many"]
|
||||
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
self.stride = m.stride
|
||||
m.bias_init() # only run once
|
||||
@ -645,6 +648,16 @@ class YOLOv10DetectionModel(DetectionModel):
|
||||
def init_criterion(self):
|
||||
return v10DetectLoss(self)
|
||||
|
||||
|
||||
class YOLOv10SegmentationModel(DetectionModel):
|
||||
def __init__(self, cfg='yolov10n-seg.yaml', ch=3, nc=None, verbose=True):
|
||||
"""Initialize YOLOv8 segmentation model with given config and parameters."""
|
||||
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
|
||||
|
||||
def init_criterion(self):
|
||||
return v10SegmentationLoss(self)
|
||||
# return v10DetectLoss(self)
|
||||
|
||||
class Ensemble(nn.ModuleList):
|
||||
"""Ensemble of models."""
|
||||
|
||||
@ -917,10 +930,11 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[x] for x in f)
|
||||
elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
|
||||
elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect,v10Segment}:
|
||||
args.append([ch[x] for x in f])
|
||||
if m is Segment:
|
||||
if m is Segment or m is v10Segment:
|
||||
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
|
||||
|
||||
elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
|
||||
args.insert(1, [ch[x] for x in f])
|
||||
elif m is CBLinear:
|
||||
@ -1007,7 +1021,7 @@ def guess_model_task(model):
|
||||
return "classify"
|
||||
if m == "detect" or m == "v10detect":
|
||||
return "detect"
|
||||
if m == "segment":
|
||||
if m == "segment" or m == "v10segment":
|
||||
return "segment"
|
||||
if m == "pose":
|
||||
return "pose"
|
||||
|
||||
@ -191,6 +191,7 @@ class v8DetectionLoss:
|
||||
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
|
||||
|
||||
return dist2bbox(pred_dist, anchor_points, xywh=False)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
@ -250,15 +251,21 @@ class v8DetectionLoss:
|
||||
class v8SegmentationLoss(v8DetectionLoss):
|
||||
"""Criterion class for computing training losses."""
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
def __init__(self, model,tal_topk=10): # model must be de-paralleled
|
||||
"""Initializes the v8SegmentationLoss class, taking a de-paralleled model as argument."""
|
||||
super().__init__(model)
|
||||
super().__init__(model,tal_topk)
|
||||
self.overlap = model.args.overlap_mask
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate and return the loss for the YOLO model."""
|
||||
|
||||
loss = torch.zeros(4, device=self.device) # box, cls, dfl
|
||||
feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
|
||||
feats = feats[1] if isinstance(feats, tuple) else feats
|
||||
|
||||
# [4, 65, 80, 80] [4, 65, 40, 40] [4, 65, 20, 20]
|
||||
# [4, 32, 8400] [4, 32, 160, 160]
|
||||
|
||||
batch_size, _, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
|
||||
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
|
||||
(self.reg_max * 4, self.nc), 1
|
||||
@ -288,7 +295,6 @@ class v8SegmentationLoss(v8DetectionLoss):
|
||||
"correctly formatted 'segment' dataset using 'data=coco8-seg.yaml' "
|
||||
"as an example.\nSee https://docs.ultralytics.com/datasets/segment/ for help."
|
||||
) from e
|
||||
|
||||
# Pboxes
|
||||
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
|
||||
|
||||
@ -725,3 +731,26 @@ class v10DetectLoss:
|
||||
one2one = preds["one2one"]
|
||||
loss_one2one = self.one2one(one2one, batch)
|
||||
return loss_one2many[0] + loss_one2one[0], torch.cat((loss_one2many[1], loss_one2one[1]))
|
||||
|
||||
|
||||
|
||||
|
||||
class v10SegmentationLoss:
|
||||
"""Criterion class for computing training losses."""
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
"""Initializes the v8SegmentationLoss class, taking a de-paralleled model as argument."""
|
||||
self.overlap = model.args.overlap_mask
|
||||
self.one2many = v8SegmentationLoss(model, tal_topk=10)
|
||||
self.one2one = v8SegmentationLoss(model, tal_topk=1)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate and return the loss for the YOLO model."""
|
||||
one2many = [preds["one2many"],preds["coef"],preds["proto"]]
|
||||
loss_one2many = self.one2many(one2many, batch)
|
||||
one2one = [preds["one2one"],preds["coef"],preds["proto"]]
|
||||
loss_one2one = self.one2one(one2one, batch)
|
||||
return loss_one2many[0] + loss_one2one[0], torch.cat((loss_one2many[1], loss_one2one[1]))
|
||||
|
||||
|
||||
|
||||
@ -851,7 +851,7 @@ def clean_str(s):
|
||||
def v10postprocess(preds, max_det, nc=80):
|
||||
assert(4 + nc == preds.shape[-1])
|
||||
boxes, scores = preds.split([4, nc], dim=-1)
|
||||
max_scores = scores.amax(dim=-1)
|
||||
max_scores = scores.amax(dim=-1)
|
||||
max_scores, index = torch.topk(max_scores, max_det, dim=-1)
|
||||
index = index.unsqueeze(-1)
|
||||
boxes = torch.gather(boxes, dim=1, index=index.repeat(1, 1, boxes.shape[-1]))
|
||||
@ -861,4 +861,26 @@ def v10postprocess(preds, max_det, nc=80):
|
||||
labels = index % nc
|
||||
index = index // nc
|
||||
boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1]))
|
||||
return boxes, scores, labels
|
||||
return boxes, scores, labels
|
||||
|
||||
def v10segpostprocess(preds, max_det, nc=80):
|
||||
proto,coef = None,None
|
||||
proto_w,proto_h = 0,0
|
||||
if isinstance(preds,list):
|
||||
preds,coef,proto = preds
|
||||
batch,cc,proto_h,proto_w = proto.shape
|
||||
assert(4 + nc == preds.shape[-1])
|
||||
boxes, scores = preds.split([4, nc], dim=-1)
|
||||
max_scores = scores.amax(dim=-1)
|
||||
max_scores, index = torch.topk(max_scores, max_det, dim=-1)
|
||||
index = index.unsqueeze(-1)
|
||||
boxes = torch.gather(boxes, dim=1, index=index.repeat(1, 1, boxes.shape[-1]))
|
||||
scores = torch.gather(scores, dim=1, index=index.repeat(1, 1, scores.shape[-1]))
|
||||
coef = torch.gather(coef, dim=1, index=index.repeat(1, 1, coef.shape[-1])) # [1,32,300]
|
||||
segmask = torch.matmul(coef, proto.reshape(batch,cc,-1)).reshape(batch, max_det,proto_h,proto_w)
|
||||
scores, index = torch.topk(scores.flatten(1), max_det, dim=-1)
|
||||
labels = index % nc
|
||||
index = index // nc
|
||||
boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1]))
|
||||
|
||||
return boxes, scores, labels,segmask
|
||||
@ -301,6 +301,7 @@ def make_anchors(feats, strides, grid_cell_offset=0.5):
|
||||
sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset # shift x
|
||||
sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset # shift y
|
||||
sy, sx = torch.meshgrid(sy, sx, indexing="ij") if TORCH_1_10 else torch.meshgrid(sy, sx)
|
||||
# print("torch.stack((sx, sy), -1).view(-1, 2) = ",torch.stack((sx, sy), -1).view(-1, 2).shape)
|
||||
anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
|
||||
stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
|
||||
return torch.cat(anchor_points), torch.cat(stride_tensor)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user