🔀 [Merge] branch 'SETUP'

.github/pull_request_template.md

@@ -0,0 +1,24 @@
+## Description
+
+[Please include a summary of the changes and the related issue. (Just overwrite this session directly)]
+
+## Type of change
+
+Please delete options that are not relevant.
+
+- [ ] Bug fix (non-breaking change which fixes an issue)
+- [ ] New feature (non-breaking change which adds functionality)
+- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
+- [ ] This change requires a documentation update
+
+## Checklist:
+
+- [ ] The code follows the Python style guide.
+- [ ] Code and files are well organized.
+- [ ] All tests pass.
+- [ ] New code is covered by tests.
+- [ ] [Optional] We would be very happy if gitmoji :technologist: could be used to assist the commit message :speech_balloon:!
+
+## Additional Information
+
+Add any other information about the PR here.

README.md

@@ -6,17 +6,19 @@
 > 
 > Use of this code is at your own risk and discretion. It is advisable to consult with the project owner before deploying or integrating into any critical systems.
 
-Welcome to the official implementation of the YOLOv7 and YOLOv9. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.
+Welcome to the official implementation of YOLOv7 and YOLOv9. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.
 
 ## TL;DR
-- Official YOLOv9 model implementation.
-- Features real-time detection with state-of-the-art accuracy.
-<!-- - Includes pre-trained models and training scripts. -->
-- Quick train: `python examples/example_train.py`
+- This is the official YOLO model implementation with an MIT License.
+- For quick deployment: you can enter directly in the terminal:
+```shell
+$pip install git+https://github.com/WongKinYiu/yolov9mit.git
+$yolo task=inference task.source=0 # source could be a single file, video, image folder, webcam ID
+```
 
 ## Introduction
 - [**YOLOv9**: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
-- [**YOLOv7**: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
+- [**YOLOv7**: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors](https://arxiv.org/abs/2207.02696)
 
 ## Installation
 To get started with YOLOv9, clone this repository and install the required dependencies:
@@ -26,43 +28,51 @@ cd yolov9mit
 pip install -r requirements.txt
 ```
 
-<!-- 
-```
-pip install git+https://github.com/WongKinYiu/yolov9mit.git
-``` 
--->
-
-<!-- ### Quick Start
-Run YOLOv9 on a pre-trained model with:
-
-```shell
-python examples/example_train.py hyper.data.batch_size=8
-``` -->
-
-<!-- ## Model Zoo[WIP]
-Find pre-trained models with benchmarks on various datasets in the [Model Zoo](docs/MODELS). -->
+## Features
+- [x] Autodownload weights/datasets
+- [x] Pip installable
+- [x] Support for devices:
+  - [x] CUDA
+  - [x] MPS (PyTorch 2.3+)
+  - [x] CPU
+- [x] Task:
+  - [x] Training
+  - [x] Inference
+  - [ ] Validation
+
+## Task
+These are simple examples. For more customization details, please refer to [Notebooks](examples) and lower-level modifications **[HOWTO](docs/HOWTO)**.
 
 ## Training
-For training YOLOv9 on your dataset:
-
-Modify the configuration file data/config.yaml to point to your dataset.
-Run the training script:
+To train YOLOv9 on your dataset:
 
+1. Modify the configuration file `data/config.yaml` to point to your dataset.
+2. Run the training script:
 ```shell
-python examples/example_train.py hyper.data.batch_size=8 model=v9-c
+python lazy.py task=train task.batch_size=8 model=v9-c
 ```
 
-More customization details, or ways to modify the model can be found [HOWTO](docs/HOWTO).
+### Transfer Learning
+To perform transfer learning with YOLOv9:
+```shell
+python lazy.py task=train task.batch_size=8 model=v9-c task.dataset={dataset_config}
+```
 
-## Evaluation [WIP]
-Evaluate the model performance using:
+### Inference
+To evaluate the model performance, use:
+```shell
+python lazy.py weights=v9-c.pt # if cloned from GitHub
+yolo task=inference task.source={Any} # if pip installed
+```
 
+### Validation [WIP]
+To validate the model performance, use:
 ```shell
-python examples/examples_evaluate.py weights=v9-c.pt
+# Work In Progress...
 ```
 
 ## Contributing
-Contributions to the YOLOv9 project are welcome! See [CONTRIBUTING](docs/CONTRIBUTING.md) for how to help out.
+Contributions to the YOLOv9 project are welcome! See [CONTRIBUTING](docs/CONTRIBUTING.md) for guidelines on how to contribute.
 
 ## Star History
 [![Star History Chart](https://api.star-history.com/svg?repos=WongKinYiu/yolov9mit&type=Date)](https://star-history.com/#WongKinYiu/yolov9mit&Date)

pyproject.toml

@@ -2,6 +2,15 @@
 name = "yolo"
 version = "0.1.0"
 dynamic = ["dependencies"]
+description = "Official Implementation of YOLO"
+authors = [
+    { name = "Hao-Tang, Tsui", email = "[email protected]" },
+]
+readme = "README.md"
+license = { file = "LICENSE" }
+requires-python = ">=3.6"
+keywords = ["yolo", "object detection", "computer vision"]
+
 
 [tool.setuptools.dynamic]
 dependencies = {file = ["requirements.txt"]}

yolo/utils/logging_utils.py

@@ -128,5 +128,5 @@ def validate_log_directory(cfg: Config, exp_name: str):
 
     os.makedirs(save_path, exist_ok=True)
     logger.opt(colors=True).info(f"📄 Created log folder: <u><fg #808080>{save_path}</></>")
-    logger.add(os.path.join(save_path, "output.log"), backtrace=True, diagnose=True)
+    logger.add(os.path.join(save_path, "output.log"), mode="w", backtrace=True, diagnose=True)
     return save_path