Comments (3)
I can't understand your intent because there is no explanation at all of what would be wrong with a GridSample that is now available with opset>=16.
https://zenn.dev/pinto0309/scraps/2766a953754dea
https://zenn.dev/pinto0309/scraps/7d4032067d0160
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Maybe I can rephrase the question this way: I have observed that your models are much faster than my own converted ones. Did you do any model optimization?
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All of my generated models committed to this zoo have been specially optimized. All five years. Thus, for RAFT, the special optimization work required to run on the old runtime environment, which is more than two years old, was necessary. Essentially, I believe that as of 2024, the system will run at high speed without special optimization work.
However, I am not really interested in optimizing an architecture that is several years old, since RAFT is designed to be quite operationally heavy in the architecture itself.
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Related Issues (20)
- License of RAFT models HOT 3
- Blazeface onnx model HOT 1
- BodyPix on MacOS - Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER HOT 8
- TOPK operator for RKNN export HOT 1
- InstructIR
- 064_Dense_Depth seems to have wrong dimensions HOT 1
- Midas2 model on coral edge TPU HOT 1
- Difference on model outputs (tflite, openvino IR, and Onnx) in model 227_face-detection-adas-0001 HOT 1
- bad results for 342_ALIKE HOT 1
- Aborted (core dumped) for full quantized tinyhitnet model
- 091_gaze-estimation-adas-0002 network HOT 1
- Release new 303_FAN with heatmaps HOT 3
- dataset HOT 1
- 410_FaceMeshV2 quantized tflite models are not functional HOT 1
- 053_BlazePose / 058_BlazePose_Full_Keypoints source HOT 3
- How to retrain simple MLP (palm_detection_full_inf_post_192x192.onnx) model with custom hand dataset ? HOT 2
- How to retrain simple MLP (palm_detection_full_inf_post_192x192.onnx) model with custom hand dataset ? HOT 2
- MiDaS conversion script to onnx
- Mismatch between BlendshapeV2 TensorFlow and TFLite Model
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