Implementation of DETR: End-to-End Object Detection with Transformers in PyTorch. In this repo, I have used DETR model and yolo models on real-time video stream. Currently, Yolo is commonly used in real-time object detection application. DETR is a new model architecture (shown bellow) for object detection that uses transformers. Please refer to this blog post for further information.

The server.py uses datasets from webcam / ip camera or video file. This information can be changed in server.py dataclass.

@dataclass
class Config:
    source: str = "assets/walking_resized.mp4"
    view_img: bool = False
    model_type: str = "detr_resnet50"
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    skip: int = 1
    yolo: bool = True
    yolo_type = "yolov8n.pt"

First, install the requirements:

pip install -r requirements.txt

Then, run the server:

python server.py

First, build the docker image:

docker build -t detr-vision .

Then, run the docker container:

docker run -it --rm --name detr-vision-container detr-vision

Run with docker-compose:

docker-compose up --build

stop the container with docker-compose:

docker-compose down