Comments (1)
There is no mistake.A DetectionHSV is a augmentation transformation, which takes image in BGR colorspace, transform it to HSV colorspace and apply random shifts to H,S and V components and then convert image back to BGR. This is data augmentation transformation aimed to improve model robustness to color changes. On validation you don't want to have any sort of randomness.
from super-gradients.
Related Issues (20)
- Speed inference Time HOT 6
- Error training yolo_nas_l HOT 4
- Issues with Bounding Box Coordinates Exceeding Image Dimensions After ONNX Export
- DiceLoss is unknown object type HOT 2
- Any model for instance segmentation?
- DataParallel Multi-gpu training problem HOT 3
- Incorrect arguments in super_gradients/training/utils/distributed_training_utils.py HOT 1
- Correct image transforms for Yolo-NAS
- Work with keypoints for recognize some poses
- Custom metrics that depends on image_path?
- DetectionRandomAffine target-size is in wrong format HOT 2
- COCO Recipe reporting low precision
- ImportError: cannot import name 'utils' from partially initialized module 'super_gradients.training' (most likely due to a circular import HOT 3
- yolo-nas-sat model availability
- AttributeError: 'RegSeg48' object has no attribute 'set_dataset_processing_params' HOT 1
- How to set different weight decay values for different modules of the model
- yolo nas pose demo/colab is broken
- How to get edge_links, edge_colors, keypoint_colors when using yolo nas pose onnx?
- Validation metrics = 0.0 during training yolo-nas
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from super-gradients.