diff --git a/docs/hub.md b/docs/hub.md
index f7a7dbb2..1ae00b1c 100644
--- a/docs/hub.md
+++ b/docs/hub.md
@@ -1,30 +1,54 @@
# Ultralytics HUB
-<div align="center">
- <a href="https://hub.ultralytics.com" target="_blank">
- <img width="1024" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<br>
+<br>
+<div align="center">
+ <a href="https://github.com/ultralytics" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+ <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+ <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+ <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+ <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+ <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+ <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+ <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
+ <br>
+ <br>
<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
+ <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
+ <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
</div>
-
+<br>
[Ultralytics HUB](https://hub.ultralytics.com) is a new no-code online tool developed
by [Ultralytics](https://ultralytics.com), the creators of the popular [YOLOv5](https://github.com/ultralytics/yolov5)
-object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLOv5 models
+object detection and image segmentation models. With Ultralytics HUB, users can easily train and deploy YOLO models
without any coding or technical expertise.
Ultralytics HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to
easily upload their data and select their model configurations. It also offers a range of pre-trained models and
templates to choose from, making it easy for users to get started with training their own models. Once a model is
trained, it can be easily deployed and used for real-time object detection and image segmentation tasks. Overall,
-Ultralytics HUB is an essential tool for anyone looking to use YOLOv5 for their object detection and image segmentation
+Ultralytics HUB is an essential tool for anyone looking to use YOLO for their object detection and image segmentation
projects.
**[Get started now](https://hub.ultralytics.com)** and experience the power and simplicity of Ultralytics HUB for
-yourself. Sign up for a free account and
-start building, training, and deploying YOLOv5 and YOLOv8 models today.
+yourself. Sign up for a free account and start building, training, and deploying YOLOv5 and YOLOv8 models today.
## 1. Upload a Dataset
@@ -44,7 +68,9 @@ zip -r coco6.zip coco6
The example [coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip) dataset in this repository can be
downloaded and unzipped to see exactly how to structure your custom dataset.
-<p align="center"><img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" /></p>
+<p align="center">
+<img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" />
+</p>
The dataset YAML is the same standard YOLOv5 YAML format. See
the [YOLOv5 Train Custom Data tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) for full details.
@@ -68,20 +94,21 @@ names:
After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab.
Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 models on it!
-<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/198611715-540c9856-49d7-4069-a2fd-7c9eb70e772e.png">
+<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">
## 2. Train a Model
-Connect to the Ultralytics HUB notebook and use your model API key to begin
-training! <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+Connect to the Ultralytics HUB notebook and use your model API key to begin training!
+
+<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
+<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
## 3. Deploy to Real World
Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
-models directly on your mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
-
-<a href="https://ultralytics.com/app_install" target="_blank">
-<img width="100%" alt="Ultralytics mobile app" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
+models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
+[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading
+the [Ultralytics App](https://ultralytics.com/app_install)!
## ❓ Issues
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index a5841b5f..6520f00d 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
-__version__ = "8.0.31"
+__version__ = "8.0.32"
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils import ops
diff --git a/ultralytics/hub/session.py b/ultralytics/hub/session.py
index 883d06a9..f6e87855 100644
--- a/ultralytics/hub/session.py
+++ b/ultralytics/hub/session.py
@@ -12,7 +12,7 @@ from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_
from ultralytics.yolo.utils import is_colab, threaded, LOGGER, emojis, PREFIX
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
-AGENT_NAME = (f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local")
+AGENT_NAME = f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local"
session = None
@@ -95,7 +95,8 @@ class HubTrainingSession:
if data.get("status", None) == "trained":
raise ValueError(
- emojis(f"Model trained. View model at https://hub.ultralytics.com/models/{self.model_id} 🚀"))
+ emojis(f"Model is already trained and uploaded to "
+ f"https://hub.ultralytics.com/models/{self.model_id} 🚀"))
if not data.get("data", None):
raise ValueError("Dataset may still be processing. Please wait a minute and try again.") # RF fix
diff --git a/ultralytics/hub/utils.py b/ultralytics/hub/utils.py
index eec139f1..f2cff500 100644
--- a/ultralytics/hub/utils.py
+++ b/ultralytics/hub/utils.py
@@ -190,5 +190,4 @@ class Traces:
# Run below code on hub/utils init -------------------------------------------------------------------------------------
-
traces = Traces()
diff --git a/ultralytics/yolo/cfg/__init__.py b/ultralytics/yolo/cfg/__init__.py
index ec1885fa..33a2e4cc 100644
--- a/ultralytics/yolo/cfg/__init__.py
+++ b/ultralytics/yolo/cfg/__init__.py
@@ -49,19 +49,19 @@ CLI_HELP_MSG = \
GitHub: https://github.com/ultralytics/ultralytics
"""
-CFG_FLOAT_KEYS = {'warmup_epochs', 'box', 'cls', 'dfl'}
+CFG_FLOAT_KEYS = {'warmup_epochs', 'box', 'cls', 'dfl', 'degrees', 'shear'}
CFG_FRACTION_KEYS = {
'dropout', 'iou', 'lr0', 'lrf', 'momentum', 'weight_decay', 'warmup_momentum', 'warmup_bias_lr', 'fl_gamma',
- 'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'degrees', 'translate', 'scale', 'shear', 'perspective', 'flipud',
- 'fliplr', 'mosaic', 'mixup', 'copy_paste', 'conf', 'iou'}
+ 'label_smoothing', 'hsv_h', 'hsv_s', 'hsv_v', 'translate', 'scale', 'perspective', 'flipud', 'fliplr', 'mosaic',
+ 'mixup', 'copy_paste', 'conf', 'iou'}
CFG_INT_KEYS = {
'epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic', 'mask_ratio', 'max_det', 'vid_stride',
'line_thickness', 'workspace', 'nbs'}
CFG_BOOL_KEYS = {
- 'save', 'cache', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect',
- 'cos_lr', 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt',
- 'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks',
- 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader'}
+ 'save', 'exist_ok', 'pretrained', 'verbose', 'deterministic', 'single_cls', 'image_weights', 'rect', 'cos_lr',
+ 'overlap_mask', 'val', 'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf',
+ 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
+ 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader'}
def cfg2dict(cfg):
diff --git a/ultralytics/yolo/data/base.py b/ultralytics/yolo/data/base.py
index 06347fa0..da321ccf 100644
--- a/ultralytics/yolo/data/base.py
+++ b/ultralytics/yolo/data/base.py
@@ -28,7 +28,6 @@ class BaseDataset(Dataset):
self,
img_path,
imgsz=640,
- label_path=None,
cache=False,
augment=True,
hyp=None,
@@ -42,7 +41,6 @@ class BaseDataset(Dataset):
super().__init__()
self.img_path = img_path
self.imgsz = imgsz
- self.label_path = label_path
self.augment = augment
self.single_cls = single_cls
self.prefix = prefix
diff --git a/ultralytics/yolo/data/build.py b/ultralytics/yolo/data/build.py
index 3448232e..4cd59832 100644
--- a/ultralytics/yolo/data/build.py
+++ b/ultralytics/yolo/data/build.py
@@ -61,7 +61,7 @@ def seed_worker(worker_id):
random.seed(worker_seed)
-def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_path=None, rank=-1, mode="train"):
+def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, rank=-1, mode="train"):
assert mode in ["train", "val"]
shuffle = mode == "train"
if cfg.rect and shuffle:
@@ -70,9 +70,8 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_pat
with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
dataset = YOLODataset(
img_path=img_path,
- label_path=label_path,
imgsz=cfg.imgsz,
- batch_size=batch_size,
+ batch_size=batch,
augment=mode == "train", # augmentation
hyp=cfg, # TODO: probably add a get_hyps_from_cfg function
rect=cfg.rect or rect, # rectangular batches
@@ -82,18 +81,19 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_pat
pad=0.0 if mode == "train" else 0.5,
prefix=colorstr(f"{mode}: "),
use_segments=cfg.task == "segment",
- use_keypoints=cfg.task == "keypoint")
+ use_keypoints=cfg.task == "keypoint",
+ names=names)
- batch_size = min(batch_size, len(dataset))
+ batch = min(batch, len(dataset))
nd = torch.cuda.device_count() # number of CUDA devices
workers = cfg.workers if mode == "train" else cfg.workers * 2
- nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
+ nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers]) # number of workers
sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
loader = DataLoader if cfg.image_weights or cfg.close_mosaic else InfiniteDataLoader # allow attribute updates
generator = torch.Generator()
generator.manual_seed(6148914691236517205 + RANK)
return loader(dataset=dataset,
- batch_size=batch_size,
+ batch_size=batch,
shuffle=shuffle and sampler is None,
num_workers=nw,
sampler=sampler,
diff --git a/ultralytics/yolo/data/dataset.py b/ultralytics/yolo/data/dataset.py
index a6f52018..fc58f6c4 100644
--- a/ultralytics/yolo/data/dataset.py
+++ b/ultralytics/yolo/data/dataset.py
@@ -14,7 +14,7 @@ from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image
class YOLODataset(BaseDataset):
- cache_version = 1.0 # dataset labels *.cache version, >= 1.0 for YOLOv8
+ cache_version = '1.0.1' # dataset labels *.cache version, >= 1.0.0 for YOLOv8
rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
"""YOLO Dataset.
Args:
@@ -22,28 +22,26 @@ class YOLODataset(BaseDataset):
prefix (str): prefix.
"""
- def __init__(
- self,
- img_path,
- imgsz=640,
- label_path=None,
- cache=False,
- augment=True,
- hyp=None,
- prefix="",
- rect=False,
- batch_size=None,
- stride=32,
- pad=0.0,
- single_cls=False,
- use_segments=False,
- use_keypoints=False,
- ):
+ def __init__(self,
+ img_path,
+ imgsz=640,
+ cache=False,
+ augment=True,
+ hyp=None,
+ prefix="",
+ rect=False,
+ batch_size=None,
+ stride=32,
+ pad=0.0,
+ single_cls=False,
+ use_segments=False,
+ use_keypoints=False,
+ names=None):
self.use_segments = use_segments
self.use_keypoints = use_keypoints
+ self.names = names
assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."
- super().__init__(img_path, imgsz, label_path, cache, augment, hyp, prefix, rect, batch_size, stride, pad,
- single_cls)
+ super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
def cache_labels(self, path=Path("./labels.cache")):
# Cache dataset labels, check images and read shapes
@@ -56,7 +54,7 @@ class YOLODataset(BaseDataset):
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(func=verify_image_label,
iterable=zip(self.im_files, self.label_files, repeat(self.prefix),
- repeat(self.use_keypoints)))
+ repeat(self.use_keypoints), repeat(len(self.names))))
pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT)
for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
nm += nm_f
diff --git a/ultralytics/yolo/data/utils.py b/ultralytics/yolo/data/utils.py
index 91d8fe02..e9ec668a 100644
--- a/ultralytics/yolo/data/utils.py
+++ b/ultralytics/yolo/data/utils.py
@@ -61,7 +61,7 @@ def exif_size(img):
def verify_image_label(args):
# Verify one image-label pair
- im_file, lb_file, prefix, keypoint = args
+ im_file, lb_file, prefix, keypoint, num_cls = args
# number (missing, found, empty, corrupt), message, segments, keypoints
nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", [], None
try:
@@ -97,16 +97,20 @@ def verify_image_label(args):
assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
kpts = np.zeros((lb.shape[0], 39))
for i in range(len(lb)):
- kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5,
- 3)) # remove the occlusion parameter from the GT
+ kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5, 3)) # remove occlusion param from GT
kpts[i] = np.hstack((lb[i, :5], kpt))
lb = kpts
assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion parameter"
else:
assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
- assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
- assert (lb[:, 1:] <=
- 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
+ assert (lb[:, 1:] <= 1).all(), \
+ f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
+ # All labels
+ max_cls = int(lb[:, 0].max()) # max label count
+ assert max_cls <= num_cls, \
+ f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
+ f'Possible class labels are 0-{num_cls - 1}'
+ assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
_, i = np.unique(lb, axis=0, return_index=True)
if len(i) < nl: # duplicate row check
lb = lb[i] # remove duplicates
@@ -192,8 +196,8 @@ def check_det_dataset(dataset, autodownload=True):
# Download (optional)
extract_dir = ''
if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
- download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1)
- data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
+ new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False)
+ data = next((DATASETS_DIR / new_dir).rglob('*.yaml'))
extract_dir, autodownload = data.parent, False
# Read yaml (optional)
diff --git a/ultralytics/yolo/engine/exporter.py b/ultralytics/yolo/engine/exporter.py
index a70a68a3..442ae171 100644
--- a/ultralytics/yolo/engine/exporter.py
+++ b/ultralytics/yolo/engine/exporter.py
@@ -203,7 +203,7 @@ class Exporter:
self.im = im
self.model = model
self.file = file
- self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else (x.shape for x in y)
+ self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y)
self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
self.metadata = {
'description': f"Ultralytics {self.pretty_name} model trained on {self.model.args['data']}",
@@ -213,8 +213,8 @@ class Exporter:
'stride': int(max(model.stride)),
'names': model.names} # model metadata
- LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
- f"output shape {self.output_shape} ({file_size(file):.1f} MB)")
+ LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} BCHW and "
+ f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)")
# Exports
f = [''] * len(fmts) # exported filenames
@@ -234,19 +234,22 @@ class Exporter:
nms = False
f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
agnostic_nms=self.args.agnostic_nms or tfjs)
- if pb or tfjs: # pb prerequisite to tfjs
- f[6], _ = self._export_pb(s_model)
- if tflite or edgetpu:
- f[7], _ = self._export_tflite(s_model,
- int8=self.args.int8 or edgetpu,
- data=self.args.data,
- nms=nms,
- agnostic_nms=self.args.agnostic_nms)
- if edgetpu:
- f[8], _ = self._export_edgetpu()
- self._add_tflite_metadata(f[8] or f[7], num_outputs=len(self.output_shape))
- if tfjs:
- f[9], _ = self._export_tfjs()
+
+ debug = False
+ if debug:
+ if pb or tfjs: # pb prerequisite to tfjs
+ f[6], _ = self._export_pb(s_model)
+ if tflite or edgetpu:
+ f[7], _ = self._export_tflite(s_model,
+ int8=self.args.int8 or edgetpu,
+ data=self.args.data,
+ nms=nms,
+ agnostic_nms=self.args.agnostic_nms)
+ if edgetpu:
+ f[8], _ = self._export_edgetpu()
+ self._add_tflite_metadata(f[8] or f[7], num_outputs=len(self.output_shape))
+ if tfjs:
+ f[9], _ = self._export_tfjs()
if paddle: # PaddlePaddle
f[10], _ = self._export_paddle()
diff --git a/ultralytics/yolo/engine/validator.py b/ultralytics/yolo/engine/validator.py
index b3e8f587..e57f3bb0 100644
--- a/ultralytics/yolo/engine/validator.py
+++ b/ultralytics/yolo/engine/validator.py
@@ -120,7 +120,7 @@ class BaseValidator:
if not pt:
self.args.rect = False
self.dataloader = self.dataloader or \
- self.get_dataloader(self.data.get("val") or self.data.set("test"), self.args.batch)
+ self.get_dataloader(self.data.get("val") or self.data.get("test"), self.args.batch)
model.eval()
model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup
diff --git a/ultralytics/yolo/utils/downloads.py b/ultralytics/yolo/utils/downloads.py
index b7b6d746..1a5f49e1 100644
--- a/ultralytics/yolo/utils/downloads.py
+++ b/ultralytics/yolo/utils/downloads.py
@@ -39,6 +39,7 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
for f in zipObj.namelist(): # list all archived filenames in the zip
if all(x not in f for x in exclude):
zipObj.extract(f, path=path)
+ return zipObj.namelist()[0] # return unzip dir
def safe_download(url,
@@ -112,13 +113,14 @@ def safe_download(url,
unzip_dir = dir or f.parent # unzip to dir if provided else unzip in place
LOGGER.info(f'Unzipping {f} to {unzip_dir}...')
if f.suffix == '.zip':
- unzip_file(file=f, path=unzip_dir) # unzip
+ unzip_dir = unzip_file(file=f, path=unzip_dir) # unzip
elif f.suffix == '.tar':
subprocess.run(['tar', 'xf', f, '--directory', unzip_dir], check=True) # unzip
elif f.suffix == '.gz':
subprocess.run(['tar', 'xfz', f, '--directory', unzip_dir], check=True) # unzip
if delete:
f.unlink() # remove zip
+ return unzip_dir
def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py
index c5b3a8b7..c199d228 100644
--- a/ultralytics/yolo/v8/detect/train.py
+++ b/ultralytics/yolo/v8/detect/train.py
@@ -41,7 +41,7 @@ class DetectionTrainer(BaseTrainer):
shuffle=mode == "train",
seed=self.args.seed)[0] if self.args.v5loader else \
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode,
- rect=mode == "val")[0]
+ rect=mode == "val", names=self.data['names'])[0]
def preprocess_batch(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py
index bc2148dc..f093b229 100644
--- a/ultralytics/yolo/v8/detect/val.py
+++ b/ultralytics/yolo/v8/detect/val.py
@@ -176,7 +176,8 @@ class DetectionValidator(BaseValidator):
prefix=colorstr(f'{self.args.mode}: '),
shuffle=False,
seed=self.args.seed)[0] if self.args.v5loader else \
- build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
+ build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, names=self.data['names'],
+ mode="val")[0]
def plot_val_samples(self, batch, ni):
plot_images(batch["img"],