The YOLOv5 model has been trained with combination of WOSDETC Drone-Vs-Bird detection competition and Det-Fly datasets in September 2021.

YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

See the YOLOv5 Docs for full documentation on training, testing and deployment.

Install: Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ cd yolov5 $ pip install -r requirements.txt

Inference with detect.py:

detect.py runs inference on a variety of sources and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                        img.jpg  # image
                        vid.mp4  # video
                        path/  # directory
                        path/*.jpg  # glob
                        'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                        'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
                        
                        
$ python detect.py --weight ./best.pt --source 'path/to/the/video' --augment --save-txt --save-conf --device=0,1,2,3 --img 1280




--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam'
--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w'
--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu'
--save-txt', action='store_true', help='save results to *.txt'
--save-conf', action='store_true', help='save confidences in --save-txt labels'
--save-crop', action='store_true', help='save cropped prediction boxes'
--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3'
--augment', action='store_true', help='augmented inference'\
--project', default=ROOT / 'runs/detect', help='save results to project/name'
--name', default='exp', help='save results to project/name'

Please fork/download the YOLOv5 or this repository and then download the weight from here and put it anywhere you want. You should change "--weight ./best.pt" parameter of detect.py module to anywhere that you put the weight.