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.
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.
- This is the official YOLO model implementation with an MIT License.
- For quick deployment: you can directly install by pip+git:
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- YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
- YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
To get started using YOLOv9's developer mode, we recommand you clone this repository and install the required dependencies:
git clone [email protected]:WongKinYiu/YOLO.git
cd YOLO
pip install -r requirements.txt|
These are simple examples. For more customization details, please refer to Notebooks and lower-level modifications HOWTO. To train YOLO on your machine/dataset:
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 argsTo perform transfer learning with YOLOv9: python yolo/lazy.py task=train task.data.batch_size=8 model=v9-c dataset={dataset_config} device={cpu, mps, cuda}To use a model for object detection, use: 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 \ # file, dir, webcam
+quite=True \ # Quite Output
yolo task.data.source={Any Source} # if pip installed
yolo task=inference task.data.source={Any}To validate model performance, or generate a json file in COCO format: python yolo/lazy.py task=validation
python yolo/lazy.py task=validation dataset=toyContributions to the YOLO project are welcome! See CONTRIBUTING for guidelines on how to contribute. flowchart TB
subgraph Features
Taskv7-->Segmentation["#35 Segmentation"]
Taskv7-->Classification["#34 Classification"]
Taskv9-->Segmentation
Taskv9-->Classification
Trainv7
end
subgraph Model
MODELv7-->v7-X
MODELv7-->v7-E6
MODELv7-->v7-E6E
MODELv9-->v9-T
MODELv9-->v9-S
MODELv9-->v9-E
end
subgraph Bugs
Fix-->Fix1["#12 mAP > 1"]
Fix-->Fix2["v9 Gradient Bump"]
Reply-->Reply1["#39"]
Reply-->Reply2["#36"]
end
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