https://aidea-web.tw/topic/9c88c428-0aa7-480b-85e0-2d8fb2fcf3fc?focus=team
Original:- Some are 2000 x 3000, some are 1728 x 2304
- Original csv data are labeled in x, y format
- split train/val manually
- `yolov5/myutils/image_cutter.py`
- `yolov5/myutils/csv2yolo.py`
-
TODO
- Add a green-only-channel(or only)
- Try custom mean/variance for normalization
- Model with big kernel(100 x 100) to find the patterns of the grid.
- Cut without resizing
- Larger bbox for d-img
- Decrease weight of cls loss in total loss
- yolo 1280
- Train all
- Use DBSCAN to filter extreme points
-
Tried and beneficial
- Merge the edge
- Can be further improved by sliding window + nms
- Decrease IOU threshold
- Decrease conf threshold
- Add hsv augmentation to detect rice in very bright/dark situations
- Does not help since bright/dark situations won't be labeled in the ground truth
- Merge the edge
-
Tried but doesn't help
- Use different detectors accordingly
- Stride training
- 不要一開始就找完美解答
- 大model != 高準確度