Comments (1)
Hello,
If you already have a torch data_loader implementation for your data- you can pass it to DatasetInterface (see or documentation).
After that, try and follow the same procedure you see in our walkthrough notebook example or our quickstart example.
Please let us know if you run into any problems.
from super-gradients.
Related Issues (20)
- Change Quantization Precision HOT 1
- knowledge distillation to object detection(YOLONAS) HOT 1
- Clarification on license for modifications to Yolo-NAS with pre-trained weights HOT 6
- 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
- SSD MobileNet V2 recipe HOT 1
- Any model for instance segmentation?
- DataParallel Multi-gpu training problem HOT 4
- 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 HOT 1
- 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 4
- 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
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