Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Unified cli method and updated docs #170

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
+8 −11
Open

Unified cli method and updated docs #170

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 8 additions & 11 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
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
Expand Down