Read the paper: https://doi.org/10.3390/rs12152501
YOLO-Fine[1] is a novel one-stage detector for detecting and classifying small vehicles from aerial and satellite imagery.
We trained YOLO-Fine[1] on the VEDAI (https://downloads.greyc.fr/vedai/) dataset. Anchors were generated using k-means clustering, and can be found in the ./vedai_anchors.txt file. Classes were set to 0 to focus on bounding box classification.
Run ./yolo_image.py
python ./yolo_image.py
Modify train.py with your:
- Annotations file
- Anchors file
- Classes file
- Input shape
- Training epochs
- Target batch size
Then run ./train.py
Note that the model will train with the darknet body frozen, and will then unfreeze those layers and continue training.
python ./yolo_image.py
We would like to thank the authors of the originial YOLO-Fine paper as well as @pjreddie and @qqwweee.
[1] Pham, Minh-Tan et al. “YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images.” Remote Sensing 12.15 (2020): 2501. Available: http://dx.doi.org/10.3390/rs12152501.