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@ -85,9 +85,6 @@ ipython_config.py
# Profiling # Profiling
*.pclprof *.pclprof
# pyenv
.python-version
# pipenv # pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies # However, in case of collaboration, if having platform-specific dependencies or dependencies
@ -138,14 +135,12 @@ dmypy.json
.pyre/ .pyre/
# datasets and projects # datasets and projects
datasets/
runs/
wandb/ wandb/
tests/ tests/
.DS_Store .DS_Store
# Neural Network weights ----------------------------------------------------------------------------------------------- # Neural Network weights -----------------------------------------------------------------------------------------------
weights/ weights/*
*.weights *.weights
*.pt *.pt
*.pb *.pb
@ -164,3 +159,6 @@ pnnx*
# Autogenerated files for tests # Autogenerated files for tests
/ultralytics/assets/ /ultralytics/assets/
# datasets
yolov10-datasets/

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@ -0,0 +1 @@
3.9.13

225
README.md
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@ -1,7 +1,7 @@
# [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458) # YOLOv10のファインチューニング
Official PyTorch implementation of **YOLOv10**. 公式のリポジトリからフォークして、独自のデータセットでファインチューニングを行うためのリポジトリです。
<p align="center"> <p align="center">
<img src="figures/latency.svg" width=48%> <img src="figures/latency.svg" width=48%>
@ -10,35 +10,7 @@ Official PyTorch implementation of **YOLOv10**.
</p> </p>
[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\ [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\
Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\ Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding
[![arXiv](https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg)](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/jameslahm/YOLOv10) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov10) [![Transformers.js Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Transformers.js-blue)](https://huggingface.co/spaces/Xenova/yolov10-web) [![LearnOpenCV](https://img.shields.io/badge/BlogPost-blue?logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAMAAAC67D%2BPAAAALVBMVEX%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F6%2Bfn6%2Bvq3y%2BJ8rOFSne9Jm%2FQcOlr5DJ7GAAAAB3RSTlMAB2LM94H1yMxlvwAAADNJREFUCFtjZGAEAob%2FQMDIyAJl%2FmFkYmEGM%2F%2F%2BYWRmYWYCMv8BmSxYmUgKkLQhGYawAgApySgfFDPqowAAAABJRU5ErkJggg%3D%3D&logoColor=black&labelColor=gray)](https://learnopencv.com/yolov10/) [![Openbayes Demo](https://img.shields.io/static/v1?label=Demo&message=OpenBayes%E8%B4%9D%E5%BC%8F%E8%AE%A1%E7%AE%97&color=green)](https://openbayes.com/console/public/tutorials/im29uYrnIoz)
<details>
<summary>
<font size="+1">Abstract</font>
</summary>
Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance.
</details>
## Notes
- 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
- 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!
- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with class names, for ease of use.
## UPDATES 🔥
- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)
- 2024/06/01: Thanks to [NielsRogge](https://github.com/NielsRogge) and [AK](https://x.com/_akhaliq) for hosting the models on the HuggingFace Hub!
- 2024/05/31: Build [yolov10-jetson](https://github.com/Seeed-Projects/jetson-examples/blob/main/reComputer/scripts/yolov10/README.md) docker image by [youjiang](https://github.com/yuyoujiang)!
- 2024/05/31: Thanks to [mohamedsamirx](https://github.com/mohamedsamirx) for the integration with [BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library](https://colab.research.google.com/drive/1-QV2TNfqaMsh14w5VxieEyanugVBG14V?usp=sharing)!
- 2024/05/31: Thanks to [kaylorchen](https://github.com/kaylorchen) for the integration with [rk3588](https://github.com/kaylorchen/rk3588-yolo-demo)!
- 2024/05/30: Thanks to [eaidova](https://github.com/eaidova) for the integration with [OpenVINO™](https://github.com/openvinotoolkit/openvino_notebooks/blob/0ba3c0211bcd49aa860369feddffdf7273a73c64/notebooks/yolov10-optimization/yolov10-optimization.ipynb)!
- 2024/05/29: Add the gradio demo for running the models locally. Thanks to [AK](https://x.com/_akhaliq)!
- 2024/05/27: Thanks to [sujanshresstha](sujanshresstha) for the integration with [DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT.git)!
- 2024/05/26: Thanks to [CVHub520](https://github.com/CVHub520) for the integration into [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling)!
- 2024/05/26: Thanks to [DanielSarmiento04](https://github.com/DanielSarmiento04) for integrate in [c++ | ONNX | OPENCV](https://github.com/DanielSarmiento04/yolov10cpp)!
- 2024/05/25: Add [Transformers.js demo](https://huggingface.co/spaces/Xenova/yolov10-web) and onnx weights(yolov10[n](https://huggingface.co/onnx-community/yolov10n)/[s](https://huggingface.co/onnx-community/yolov10s)/[m](https://huggingface.co/onnx-community/yolov10m)/[b](https://huggingface.co/onnx-community/yolov10b)/[l](https://huggingface.co/onnx-community/yolov10l)/[x](https://huggingface.co/onnx-community/yolov10x)). Thanks to [xenova](https://github.com/xenova)!
- 2024/05/25: Add [colab demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s), [HuggingFace Demo](https://huggingface.co/spaces/kadirnar/Yolov10), and [HuggingFace Model Page](https://huggingface.co/kadirnar/Yolov10). Thanks to [SkalskiP](https://github.com/SkalskiP) and [kadirnar](https://github.com/kadirnar)!
## Performance ## Performance
COCO COCO
@ -53,60 +25,130 @@ COCO
| [YOLOv10-X](https://huggingface.co/jameslahm/yolov10x) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms | | [YOLOv10-X](https://huggingface.co/jameslahm/yolov10x) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms |
## Installation ## Installation
`conda` virtual environment is recommended.
## 環境
- pyenv
- Python 3.9.13 (公式のバージョンと合わせる)
- cuda 11.8
## Setup
### 1. リポジトリをクローン
```bash
git clone git@github.com:TechC-SugarCane/train-YOLOv10.git
cd train-YOLOv10
``` ```
conda create -n yolov10 python=3.9
conda activate yolov10 ### 2. Pythonの環境構築
pip install -r requirements.txt
```bash
pyenv install
```
### 3. 仮想環境を作成
```bash
python -m venv .venv
```
### 4. 仮想環境を有効化
```bash
# mac
source .venv/bin/activate
# windows
.venv\Scripts\activate
```
※ 環境から抜ける場合は、`deactivate`コマンドを実行してください。
### 5. 依存パッケージをインストール
```bash
# CPUで推論を行う場合
pip install -r requirements-cpu.txt
# GPUで推論を行う場合
pip install -r requirements-gpu.txt
# 共通
pip install -e . pip install -e .
``` ```
## Demo
``` ### 6. デフォルトセッティングを変更
python app.py
# Please visit http://127.0.0.1:7860 ```bash
# datasetsのディレクトリを現在のディレクトリに変更
# デフォルトだと../datasetsが設定されている
yolo settings datasets_dir=.
``` ```
## Validation ## Training
[`yolov10n`](https://huggingface.co/jameslahm/yolov10n) [`yolov10s`](https://huggingface.co/jameslahm/yolov10s) [`yolov10m`](https://huggingface.co/jameslahm/yolov10m) [`yolov10b`](https://huggingface.co/jameslahm/yolov10b) [`yolov10l`](https://huggingface.co/jameslahm/yolov10l) [`yolov10x`](https://huggingface.co/jameslahm/yolov10x)
``` 事前学習済みモデルとして`yolov10x.pt`を使用するので、[公式GitHubのリリース](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt)からダウンロードして`weights`ディレクトリに配置してください。
yolo val model=jameslahm/yolov10{n/s/m/b/l/x} data=coco.yaml batch=256
学習に使用するデータセットはRoboflowというサービスを使用して作成しています。
学習や評価に使用するデータセットは、
- [サトウキビ](https://universe.roboflow.com/hoku/sugarcane-3vhxz/dataset/11)
- [パイナップル](https://universe.roboflow.com/hoku/pineapple-thsih/dataset/7)
にありますが、手動でダウンロードするのは面倒なので`huggingface`にdatasetsをまとめてあります。
下記コマンドを実行して、datasetsをダウンロードしてください。
```bash
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/datasets/TechC-SugarCane/yolov10-datasets
# git push時に発生するエラーを無効化
git config lfs.https://github.com/TechC-SugarCane/train-YOLOv10.git/info/lfs.locksverify false
``` ```
Or 学習後の結果は`runs/detect/<name(番号)>`に保存されます。
```python
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}') 学習でよいスコアが出た場合は、`runs/detect/<name(番号)>/`にREADME.mdを作成してください。
# or その際は、[`runs/detect/README.md`](./runs/detect/README.md)を参考に作成してください。
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.val(data='coco.yaml', batch=256) ```bash
# sugarcane
yolo detect train cfg='cfg/sugarcane.yaml' data=yolov10-datasets/sugarcane/data.yaml model=weights/yolov10x.pt name='yolov10x-sugarcane' epochs=300 batch=16 imgsz=640 device=0
# pineapple
yolo detect train cfg='cfg/pineapple.yaml' data=yolov10-datasets/pineapple/data.yaml model=weights/yolov10x.pt name='yolov10x-pineapple' epochs=300 batch=16 imgsz=640 device=0
``` ```
※ 上記を実行すると`yolov8n.pt`がダウンロードされますが、AMPというものの確認用に追加されているだけらしいので気にしなくて大丈夫です。
詳しくは[#106](https://github.com/THU-MIG/yolov10/issues/106)を参照してください。
## Training ハイパーパラメーターは自由に調整してください。`cfg/`にあります。このファイルの`Hyperparameters`の部分でハイパラ関連の設定ができます。
```
yolo detect train data=coco.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7
```
Or - サトウキビ: `sugarcane.yaml`
```python - パイナップル: `pineapple.yaml`
from ultralytics import YOLOv10
model = YOLOv10() ## コントリビューター向けガイドライン
# If you want to finetune the model with pretrained weights, you could load the
# pretrained weights like below
# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640) コントリビューター向けのガイドラインについては、こちらの[CONTRIBUTING.md](https://github.com/TechC-SugarCane/.github/blob/main/CONTRIBUTING.md)を参照してください。
```
### ※ 注意
このリポジトリはforkなので、Pull Requestを送る際はこのリポジトリに対して送るようにしてください。
デフォルトだとbaseリポジトリが公式のリポジトリになっているので、注意してください。
`Comparing changes`でのドロップダウン(`base repository`)を、`TechC-SugarCane/train-YOLOv10`に変更してください。画面が遷移したら大丈夫です。
## Push to hub to 🤗 ## Push to hub to 🤗
後で活用
Optionally, you can push your fine-tuned model to the [Hugging Face hub](https://huggingface.co/) as a public or private model: Optionally, you can push your fine-tuned model to the [Hugging Face hub](https://huggingface.co/) as a public or private model:
```python ```python
@ -117,25 +159,8 @@ model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-dete
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection", private=True) model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection", private=True)
``` ```
## Prediction
Note that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to [here](https://github.com/THU-MIG/yolov10/issues/136) for details.
```
yolo predict model=jameslahm/yolov10{n/s/m/b/l/x}
```
Or
```python
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.predict()
```
## Export ## Export
後で活用
``` ```
# End-to-End ONNX # End-to-End ONNX
yolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=onnx opset=13 simplify yolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=onnx opset=13 simplify
@ -149,33 +174,3 @@ trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --
# Predict with TensorRT # Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine yolo predict model=yolov10n/s/m/b/l/x.engine
``` ```
Or
```python
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.export(...)
```
## Acknowledgement
The code base is built with [ultralytics](https://github.com/ultralytics/ultralytics) and [RT-DETR](https://github.com/lyuwenyu/RT-DETR).
Thanks for the great implementations!
## Citation
If our code or models help your work, please cite our paper:
```BibTeX
@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}
}
```

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@ -0,0 +1,127 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
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)
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)
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
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
verbose: True # (bool) whether to print verbose output
seed: 0 # (int) random seed for reproducibility
deterministic: True # (bool) whether to enable deterministic mode
single_cls: False # (bool) train multi-class data as single-class
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # (bool) use cosine learning rate scheduler
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
resume: False # (bool) resume training from last checkpoint
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation
overlap_mask: True # (bool) masks should overlap during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # (float) use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # (bool) validate/test during training
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # (bool) save results to JSON file
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
max_det: 300 # (int) maximum number of detections per image
half: False # (bool) use half precision (FP16)
dnn: False # (bool) use OpenCV DNN for ONNX inference
plots: True # (bool) save plots and images during train/val
# Predict settings -----------------------------------------------------------------------------------------------------
source: # (str, optional) source directory for images or videos
vid_stride: 1 # (int) video frame-rate stride
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
visualize: False # (bool) visualize model features
augment: False # (bool) apply image augmentation to prediction sources
agnostic_nms: False # (bool) class-agnostic NMS
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks: False # (bool) use high-resolution segmentation masks
embed: # (list[int], optional) return feature vectors/embeddings from given layers
# Visualize settings ---------------------------------------------------------------------------------------------------
show: False # (bool) show predicted images and videos if environment allows
save_frames: False # (bool) save predicted individual video frames
save_txt: False # (bool) save results as .txt file
save_conf: False # (bool) save results with confidence scores
save_crop: False # (bool) save cropped images with results
show_labels: True # (bool) show prediction labels, i.e. 'person'
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
show_boxes: True # (bool) show prediction boxes
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
keras: False # (bool) use Kera=s
optimize: False # (bool) TorchScript: optimize for mobile
int8: False # (bool) CoreML/TF INT8 quantization
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
simplify: False # (bool) ONNX: simplify model using `onnxslim`
opset: # (int, optional) ONNX: opset version
workspace: 4 # (int) TensorRT: workspace size (GB)
nms: False # (bool) CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
momentum: 0.937 # (float) SGD momentum/Adam beta1
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
warmup_momentum: 0.8 # (float) warmup initial momentum
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
box: 7.5 # (float) box loss gain
cls: 0.5 # (float) cls loss gain (scale with pixels)
dfl: 1.5 # (float) dfl loss gain
pose: 12.0 # (float) pose loss gain
kobj: 1.0 # (float) keypoint obj loss gain
label_smoothing: 0.0 # (float) label smoothing (fraction)
nbs: 64 # (int) nominal batch size
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
degrees: 0.0 # (float) image rotation (+/- deg)
translate: 0.1 # (float) image translation (+/- fraction)
scale: 0.5 # (float) image scale (+/- gain)
shear: 0.0 # (float) image shear (+/- deg)
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # (float) image flip up-down (probability)
fliplr: 0.5 # (float) image flip left-right (probability)
bgr: 0.0 # (float) image channel BGR (probability)
mosaic: 1.0 # (float) image mosaic (probability)
mixup: 0.0 # (float) image mixup (probability)
copy_paste: 0.0 # (float) segment copy-paste (probability)
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: # (str, optional) for overriding defaults.yaml
# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]

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@ -0,0 +1,127 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
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)
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)
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
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
verbose: True # (bool) whether to print verbose output
seed: 0 # (int) random seed for reproducibility
deterministic: True # (bool) whether to enable deterministic mode
single_cls: False # (bool) train multi-class data as single-class
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # (bool) use cosine learning rate scheduler
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
resume: False # (bool) resume training from last checkpoint
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation
overlap_mask: True # (bool) masks should overlap during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # (float) use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # (bool) validate/test during training
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # (bool) save results to JSON file
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
max_det: 300 # (int) maximum number of detections per image
half: False # (bool) use half precision (FP16)
dnn: False # (bool) use OpenCV DNN for ONNX inference
plots: True # (bool) save plots and images during train/val
# Predict settings -----------------------------------------------------------------------------------------------------
source: # (str, optional) source directory for images or videos
vid_stride: 1 # (int) video frame-rate stride
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
visualize: False # (bool) visualize model features
augment: False # (bool) apply image augmentation to prediction sources
agnostic_nms: False # (bool) class-agnostic NMS
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks: False # (bool) use high-resolution segmentation masks
embed: # (list[int], optional) return feature vectors/embeddings from given layers
# Visualize settings ---------------------------------------------------------------------------------------------------
show: False # (bool) show predicted images and videos if environment allows
save_frames: False # (bool) save predicted individual video frames
save_txt: False # (bool) save results as .txt file
save_conf: False # (bool) save results with confidence scores
save_crop: False # (bool) save cropped images with results
show_labels: True # (bool) show prediction labels, i.e. 'person'
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
show_boxes: True # (bool) show prediction boxes
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
keras: False # (bool) use Kera=s
optimize: False # (bool) TorchScript: optimize for mobile
int8: False # (bool) CoreML/TF INT8 quantization
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
simplify: False # (bool) ONNX: simplify model using `onnxslim`
opset: # (int, optional) ONNX: opset version
workspace: 4 # (int) TensorRT: workspace size (GB)
nms: False # (bool) CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
momentum: 0.937 # (float) SGD momentum/Adam beta1
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
warmup_momentum: 0.8 # (float) warmup initial momentum
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
box: 7.5 # (float) box loss gain
cls: 0.5 # (float) cls loss gain (scale with pixels)
dfl: 1.5 # (float) dfl loss gain
pose: 12.0 # (float) pose loss gain
kobj: 1.0 # (float) keypoint obj loss gain
label_smoothing: 0.0 # (float) label smoothing (fraction)
nbs: 64 # (int) nominal batch size
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
degrees: 0.0 # (float) image rotation (+/- deg)
translate: 0.1 # (float) image translation (+/- fraction)
scale: 0.5 # (float) image scale (+/- gain)
shear: 0.0 # (float) image shear (+/- deg)
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # (float) image flip up-down (probability)
fliplr: 0.5 # (float) image flip left-right (probability)
bgr: 0.0 # (float) image channel BGR (probability)
mosaic: 1.0 # (float) image mosaic (probability)
mixup: 0.0 # (float) image mixup (probability)
copy_paste: 0.0 # (float) segment copy-paste (probability)
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: # (str, optional) for overriding defaults.yaml
# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]

2
requirements.txt → official-requirements.txt
View File

@ -12,4 +12,4 @@ opencv-python==4.9.0.80
psutil==5.9.8 psutil==5.9.8
py-cpuinfo==9.0.0 py-cpuinfo==9.0.0
huggingface-hub==0.23.2 huggingface-hub==0.23.2
safetensors==0.4.3 safetensors==0.4.3

95
requirements-cpu.txt Normal file
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@ -0,0 +1,95 @@
aiofiles==23.2.1
altair==5.3.0
annotated-types==0.7.0
anyio==4.4.0
attrs==23.2.0
certifi==2024.7.4
charset-normalizer==3.3.2
click==8.1.7
colorama==0.4.6
coloredlogs==15.0.1
contourpy==1.2.1
cycler==0.12.1
dnspython==2.6.1
email_validator==2.2.0
exceptiongroup==1.2.2
fastapi==0.111.1
fastapi-cli==0.0.4
ffmpy==0.4.0
filelock==3.15.4
flatbuffers==24.3.25
fonttools==4.53.1
fsspec==2024.6.1
gradio==4.31.5
gradio_client==0.16.4
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.2
humanfriendly==10.0
idna==3.7
importlib_resources==6.4.0
Jinja2==3.1.4
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
kiwisolver==1.4.5
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.9.1
mdurl==0.1.2
mpmath==1.3.0
networkx==3.2.1
numpy==1.26.4
onnx==1.14.0
onnxruntime==1.15.1
onnxruntime-gpu==1.18.0
onnxslim==0.1.31
opencv-python==4.9.0.80
orjson==3.10.6
packaging==24.1
pandas==2.2.2
pillow==10.4.0
protobuf==5.27.2
psutil==5.9.8
py-cpuinfo==9.0.0
pycocotools==2.0.7
pydantic==2.8.2
pydantic_core==2.20.1
pydub==0.25.1
Pygments==2.18.0
pyparsing==3.1.2
pyreadline3==3.4.1
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
pytz==2024.1
PyYAML==6.0.1
referencing==0.35.1
requests==2.32.3
rich==13.7.1
rpds-py==0.19.1
ruff==0.5.5
safetensors==0.4.3
scipy==1.13.0
seaborn==0.13.2
semantic-version==2.10.0
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
starlette==0.37.2
sympy==1.13.1
thop==0.1.1.post2209072238
tomlkit==0.12.0
toolz==0.12.1
torch==2.0.1
torchvision==0.15.2
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
tzdata==2024.1
urllib3==2.2.2
uvicorn==0.30.3
watchfiles==0.22.0
websockets==11.0.3
zipp==3.19.2

96
requirements-gpu.txt Normal file
View File

@ -0,0 +1,96 @@
aiofiles==23.2.1
altair==5.3.0
annotated-types==0.7.0
anyio==4.4.0
attrs==23.2.0
certifi==2024.7.4
charset-normalizer==3.3.2
click==8.1.7
colorama==0.4.6
coloredlogs==15.0.1
contourpy==1.2.1
cycler==0.12.1
dnspython==2.6.1
email_validator==2.2.0
exceptiongroup==1.2.2
fastapi==0.111.1
fastapi-cli==0.0.4
ffmpy==0.3.2
filelock==3.15.4
flatbuffers==24.3.25
fonttools==4.53.1
fsspec==2024.6.1
gradio==4.31.5
gradio_client==0.16.4
h11==0.14.0
httpcore==1.0.5
httptools==0.6.1
httpx==0.27.0
huggingface-hub==0.23.2
humanfriendly==10.0
idna==3.7
importlib_resources==6.4.0
Jinja2==3.1.4
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
kiwisolver==1.4.5
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.9.0
mdurl==0.1.2
mpmath==1.3.0
networkx==3.2.1
numpy==1.26.4
onnx==1.14.0
onnxruntime-gpu==1.18.0
onnxslim==0.1.31
opencv-python==4.9.0.80
orjson==3.10.6
packaging==24.1
pandas==2.2.2
pillow==10.4.0
protobuf==5.27.2
psutil==5.9.8
py-cpuinfo==9.0.0
pycocotools==2.0.7
pydantic==2.8.2
pydantic_core==2.20.1
pydub==0.25.1
Pygments==2.18.0
pyparsing==3.1.2
pyreadline3==3.4.1
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.9
pytz==2024.1
PyYAML==6.0.1
referencing==0.35.1
requests==2.32.3
rich==13.7.1
rpds-py==0.19.0
ruff==0.5.4
safetensors==0.4.3
scipy==1.13.0
seaborn==0.13.2
semantic-version==2.10.0
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
starlette==0.37.2
sympy==1.13.1
thop==0.1.1.post2209072238
tomlkit==0.12.0
toolz==0.12.1
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
tzdata==2024.1
urllib3==2.2.2
uvicorn==0.30.3
watchfiles==0.22.0
websockets==11.0.3
zipp==3.19.2
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.0.1+cu118
torchvision==0.15.2+cu118

51
runs/detect/README.md Normal file
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@ -0,0 +1,51 @@
このREADMEは工事中です
# 学習後の保存方法について
## trainのディレクトリ構造
学習後の結果は`runs/detect/<name(番号)>`に保存されます。`<name(番号)>`は学習時にコマンドで指定したnameオプションの値です。<br>
同じnameオプションの値を指定した場合、`<name(番号)>`の値がインクリメントされます。
そのディレクトリの中身は、学習結果の可視化, モデルの重みファイル, ログファイルなどが保存されています。
## 学習後について
学習でよいスコアが出た場合は、`runs/detect/<name(番号)>/`にREADME.mdを作成してください。
その際のREADME.mdのフォーマットは以下の通りです。
コマンドに学習時のコマンドを、結果に学習後のコンソール画面のスクショパス(console.png)を記載してください。
````markdown
## コマンド
```bash
# ここに学習時のコマンドを記載してください
例:
yolo detect train \
cfg='cfg/sugarcane.yaml' \
data=datasets/sugarcane/data.yaml \
model=weights/yolov10x.pt \
name='yolov10x-sugarcane' \
epochs=300 \
batch=16 \
imgsz=640 \
device=0
```
## 学習過程
![results.png](./results.png)
## 結果
![結果のスクショを同ディレクトリ内の`console.png`に保存してください](./console.png)
````
READMEの例は<<いつか上げる。それまでは[YOLOv9の実装](https://github.com/TechC-SugarCane/train-YOLOv9/tree/main/runs/train/yolov9-e-pineapple-たたき台)を参考にしてほしい>>を参照してください。
## モデルの保存
現在GitHubに上がっているスコアより良いモデルができた場合、<<たぶんfuggingface>>に`best.pt`をアップロードしてください。

2
weights/.gitignore vendored Normal file
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@ -0,0 +1,2 @@
*
!.gitignore