🚑️ [Add] Back accidentally removed README

README.md

@@ -56,3 +56,110 @@ pip install -r requirements.txt
 
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+
+## 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}
+}
+```