PINTO_model_zoo
Please read the contents of the LICENSE
file located directly under each folder before using the model. My model conversion scripts are released under the MIT license, but the license of the source model itself is subject to the license of the provider repository.
Contributors
Made with contrib.rocks.
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
TensorFlow Lite, OpenVINO, CoreML, TensorFlow.js, TF-TRT, MediaPipe, ONNX [.tflite, .h5, .pb, saved_model, tfjs, tftrt, mlmodel, .xml/.bin, .onnx]
I have been working on quantization of various models as a hobby, but I have skipped the work of making sample code to check the operation because it takes a lot of time. I welcome a pull request from volunteers to provide sample code. :smile:
[Note Jan 05, 2020] Currently, the MobileNetV3 backbone model and the Full Integer Quantization model do not return correctly.
[Note Jan 08, 2020] If you want the best performance with RaspberryPi4/3, install Ubuntu 19.10 aarch64 (64bit) instead of Raspbian armv7l (32bit). The official Tensorflow Lite is performance tuned for aarch64. On aarch64 OS, performance is about 4 times higher than on armv7l OS.
My article
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Conversion of PyTorch->ONNX->OpenVINO IR model to Tensorflow saved_model / h5 / tflite / pb
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[TF2 Object Detection] Converting SSD models into .tflite uint8 format #9371
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[Japanese] Custom Operation入りのtfliteを逆コンバートしてJSON化し標準OPへ置き換えたうえでtfliteを再生成する方法
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Add a custom OP to the TFLite runtime to build the whl installer (for Python),
MaxPoolingWithArgmax2D
,MaxUnpooling2D
,Convolution2DTransposeBias
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Inverse Quantization of tflite's Sparse Tensor Densify to Refine a Clean Float32 Model
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Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby.
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Simple tool to combine onnx models. Simple Network Combine Tool for ONNX.
List of pre-quantized models
* WQ = Weight Quantization ** OV = OpenVINO IR *** CM = CoreML **** DQ = Dynamic Range Quantization
1. Image Classification
No. | Model Name | Link | FP32 | FP16 | INT8 | DQ | TPU | WQ | OV | CM | TFJS | TF-TRT | ONNX | Remarks |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
004 | Efficientnet | ■■■ | ⚫ | ⚫ | ⚫ | |||||||||
010 | Mobilenetv3 | ■■■ | ⚫ | ⚫ | ⚫ | |||||||||
011 | Mobilenetv2 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ||||||||
016 | Efficientnet-lite | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | |||
070 | age-gender-recognition | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | |||
083 | Person_Reidentification | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 248,277,286,287,288,300 | ||
087 | DeepSort | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ||||
124 | person-attributes-recognition-crossroad-0230 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | |||
125 | person-attributes-recognition-crossroad-0234 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ||
126 | person-attributes-recognition-crossroad-0238 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ||
175 | face-recognition-resnet100-arcface-onnx | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | RGB/BGR,112x112,[1,512] | |
187 | vehicle-attributes-recognition-barrier-0039 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 72x72 |
188 | vehicle-attributes-recognition-barrier-0042 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 72x72 |
191 | anti-spoof-mn3 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 128x128 | |
192 | open-closed-eye-0001 | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 32x32 | |
194 | face_recognizer_fast | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 112x112 |
195 | person_reid_youtu | ■■■ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | 256x128, |