✏️ [Fix] typo in README doc

README.md

@@ -2,9 +2,10 @@
 
 > [!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.
 > However, inference and validation with pre-trained weights on COCO are available and can be used safely.
 
@@ -28,19 +29,24 @@
 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 directly install by pip+git:
+
 ```shell
 pip install git+https://github.com/WongKinYiu/YOLO.git
 yolo task.data.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)
 
 ## Installation
+
 To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies:
+
 ```shell
 git clone git@github.com:WongKinYiu/YOLO.git
 cd YOLO
@@ -52,48 +58,34 @@ pip install -r requirements.txt
 <table>
 <tr><td>
 
-| Tools | pip 🐍 | HuggingFace 🤗 | Docker 🐳 |
-| -------------------- | :----: | :--------------: | :-------: |
-| Compatibility       | ✅     | ✅               | 🧪        |
-
-|  Phase    | Training | Validation | Inference |
-| ------------------- | :------: | :---------: | :-------: |
-| Supported           | ✅       | ✅          | ✅        |
-
-</td><td>
-
-| Device | CUDA       | CPU       | MPS       |
-| ------------------ | :---------: | :-------: | :-------: |
-| PyTorch            | v1.12      | v2.3+     | v1.12     |
-| ONNX               | ✅         | ✅        | -         |
-| TensorRT           | ✅         | -        | -         |
-| OpenVINO           | -          | 🧪        | ❔        |
-
-</td></tr> </table>
-
-
-
 ## 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
@@ -109,16 +101,20 @@ 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
@@ -145,9 +141,11 @@ flowchart TB
 ```
 
 ## 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},