📝 [update] Doc, fix the MEM leak

README.md

@@ -3,7 +3,6 @@
 > [!CAUTION]
 > We wanted to inform you that the training code for this project is still in progress, and there are two known issues:
 >
-> - CPU memory leak during training
 > - Slower convergence speed
 >
 > We strongly recommend refraining from training the model until version 1.0 is released.
@@ -57,110 +56,3 @@ pip install -r requirements.txt
 
 <table>
 <tr><td>
-
-## Task
-
-These are simple examples. For more customization details, please refer to [Notebooks](examples) and lower-level modifications **[HOWTO](docs/HOWTO.md)**.
-
-## Training
-
-To train YOLO on your machine/dataset:
-
-1. Modify the configuration file `yolo/config/dataset/**.yaml` to point to your dataset.
-2. Run the training script:
-
-```shell
-python yolo/lazy.py task=train dataset=** use_wandb=True
-python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c weight=False # or more args
-```
-
-### Transfer Learning
-
-To perform transfer learning with YOLOv9:
-
-```shell
-python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={dataset_config} device={cpu, mps, cuda}
-```
-
-### Inference
-
-To use a model for object detection, use:
-
-```shell
-python yolo/lazy.py # if cloned from GitHub
-python yolo/lazy.py task=inference \ # default is inference
-                    name=AnyNameYouWant \ # AnyNameYouWant
-                    device=cpu \ # hardware cuda, cpu, mps
-                    model=v9-s \ # model version: v9-c, m, s
-                    task.nms.min_confidence=0.1 \ # nms config
-                    task.fast_inference=onnx \ # onnx, trt, deploy
-                    task.data.source=data/toy/images/train \ # file, dir, webcam
-                    +quite=True \ # Quite Output
-yolo task.data.source={Any Source} # if pip installed
-yolo task=inference task.data.source={Any}
-```
-
-### Validation
-
-To validate model performance, or generate a json file in COCO format:
-
-```shell
-python yolo/lazy.py task=validation
-python yolo/lazy.py task=validation dataset=toy
-```
-
-## Contributing
-
-Contributions to the YOLO project are welcome! See [CONTRIBUTING](docs/CONTRIBUTING.md) for guidelines on how to contribute.
-
-### TODO Diagrams
-
-```mermaid
-flowchart TB
-    subgraph Features
-      Taskv7-->Segmentation["#35 Segmentation"]
-      Taskv7-->Classification["#34 Classification"]
-      Taskv9-->Segmentation
-      Taskv9-->Classification
-      Trainv7
-    end
-    subgraph Model
-      MODELv7-->v7-X
-      MODELv7-->v7-E6
-      MODELv7-->v7-E6E
-      MODELv9-->v9-T
-      MODELv9-->v9-S
-      MODELv9-->v9-E
-    end
-    subgraph Bugs
-      Fix-->Fix1["#12 mAP > 1"]
-      Fix-->Fix2["v9 Gradient Bump"]
-      Reply-->Reply1["#39"]
-      Reply-->Reply2["#36"]
-    end
-```
-
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=WongKinYiu/YOLO&type=Date)](https://star-history.com/#WongKinYiu/YOLO&Date)
-
-## Citations
-
-```
-@misc{wang2022yolov7,
-      title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
-      author={Chien-Yao Wang and Alexey Bochkovskiy and Hong-Yuan Mark Liao},
-      year={2022},
-      eprint={2207.02696},
-      archivePrefix={arXiv},
-      primaryClass={id='cs.CV' full_name='Computer Vision and Pattern Recognition' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.'}
-}
-@misc{wang2024yolov9,
-      title={YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information},
-      author={Chien-Yao Wang and I-Hau Yeh and Hong-Yuan Mark Liao},
-      year={2024},
-      eprint={2402.13616},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```