By the end of this post, you will learn how to: Train a SOTA YOLOv5 model on your own data. Sparsify the model using SparseML quantization aware training, sparse transfer learning, and one-shot quantization. Export the sparsified model and run it using the DeepSparse engine at insane speeds. P/S: The end result - YOLOv5 on CPU at 180+ FPS using on
I use model yolov5m6 from yolov5 after I train and use use yolov5.transfer_learn_pruned_quantized.md I got error
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(float)) , expected: (tensor(uint8))
What should I do?
I think the problem is that I cannot use --quantized-inputs in detect.py , its only has in annotate.py that why input is not uint8