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updated readme with unified installation cli
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hardikdava committed Feb 10, 2025
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Expand Up @@ -40,9 +40,9 @@ yolo task.data.source=0 # source could be a single file, video, image folder, we
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
git clone https://github.com/MultimediaTechLab/YOLO.git
cd YOLO
pip install -r requirements.txt
pip install -e .
```

## Features
Expand All @@ -62,43 +62,40 @@ To train YOLO on your machine/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
yolo task=train dataset=** use_wandb=True
yolo 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}
yolo 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
yolo 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
yolo task=validation
yolo task=validation dataset=toy
```

## Contributing
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