✅ [Pass] test and change README& default task!

README.md

@@ -2,20 +2,20 @@
 
 ![GitHub License](https://img.shields.io/github/license/WongKinYiu/YOLO)
 [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/henry000/YOLO)
-<!-- ![WIP](https://img.shields.io/badge/status-WIP-orange) -->
+![WIP](https://img.shields.io/badge/status-WIP-orange)
 <!-- > [!IMPORTANT]
 > This project is currently a Work In Progress and may undergo significant changes. It is not recommended for use in production environments until further notice. Please check back regularly for updates.
 >
-> 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.
+> 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 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
 - This is the official YOLO model implementation with an MIT License.
-- For quick deployment: you can enter directly in the terminal:
+- For quick deployment: you can directly install by pip+git:
 ```shell
 pip install [email protected]:WongKinYiu/YOLO.git
-yolo task=inference task.source=0 # source could be a single file, video, image folder, webcam ID
+yolo task.data.source=0 # source could be a single file, video, image folder, webcam ID
 ```
 
 ## Introduction
@@ -23,7 +23,7 @@ yolo task=inference task.source=0 # source could be a single file, video, image
 - [**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:
+To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies:
 ```shell
 git clone [email protected]:WongKinYiu/YOLO.git
 cd YOLO
@@ -60,13 +60,13 @@ pip install -r requirements.txt
 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 dataset:
+To train YOLO on your machine/dataset:
 
-1. Modify the configuration file `data/config.yaml` to point to your 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 dataset=dev use_wandb=True
-python yolo/lazy.py task.data.batch_size=8 model=v9-c # or more args
+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
@@ -76,36 +76,44 @@ python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={datase
 ```
 
 ### Inference
-To evaluate the model performance, use:
+To use a model for object detection, use:
 ```shell
-python yolo/lazy.py task=inference # if cloned from GitHub
-python yolo/lazy.py task=inference \
+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 \ # path to file, dir, webcam
+                    task.data.source=data/toy/images/train \ # file, dir, webcam
                     +quite=True \ # Quite Output
-yolo task=inference task.data.source={Any} # if pip installed
+yolo task.data.source={Any Source} # if pip installed
+yolo task=inference task.data.source={Any}
 ```
 
 ### Validation
-To validate the model performance, use:
+To validate model performance, or generate a json file in COCO format:
 ```shell
 python yolo/lazy.py task=validation
-# or
 python yolo/lazy.py task=validation dataset=toy
 ```
 
 ## Contributing
-Contributions to the YOLOv9 project are welcome! See [CONTRIBUTING](docs/CONTRIBUTING.md) for guidelines on how to contribute.
+Contributions to the YOLO 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/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},

docs/HOWTO.md

@@ -6,7 +6,7 @@ To facilitate easy customization of the YOLO model, we've structured the codebas
 
 ```shell
 # Train
-python yolo/lazy.py dataset=dev use_wandb=True
+python yolo/lazy.py task=train dataset=dev use_wandb=True
 
 # Validate
 python yolo/lazy.py task=validation

tests/test_tools/test_loss_functions.py

@@ -17,7 +17,7 @@ from yolo.utils.bounding_box_utils import Vec2Box
 @pytest.fixture
 def cfg() -> Config:
     with initialize(config_path="../../yolo/config", version_base=None):
-        cfg = compose(config_name="config")
+        cfg = compose(config_name="config", overrides=["task=train"])
     return cfg
 
 

yolo/config/config.yaml

@@ -6,7 +6,7 @@ name: v9-dev
 
 defaults:
   - _self_
-  - task: train
+  - task: inference
   - dataset: coco
   - model: v9-c
   - general