Comments (5)
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from super-gradients.
What version of python are you using?
Could you please also dump pip freeze
output to see what versions of already installed packages you have? Thanks!
from super-gradients.
I would suggest remove forcely torch requirements when install, let users to define which version they want use, in 2023, mostly users are using torch2.0 (which is also stable for now).
You should eventually and actually ASAP upgrade to torcrh2.0 rather than forcely less than 1.14
from super-gradients.
I agree, that's quite restricting. Hopefully that would be resolved in a next feature update.
from super-gradients.
This is fixed in master branch already and will be available in the next version release
#944
#908
from super-gradients.
Related Issues (20)
- WANDB logging not showing HOT 1
- Enable class agnostic NMS HOT 1
- Recipe should be saved to additional_configs_to_log before instantiating it
- RuntimeError: Given groups=1, weight of size [48, 3, 3, 3], expected input[2, 640, 640, 640] to have 3 channels, but got 640 channels instead
- 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
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from super-gradients.