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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
|---|---|
| 2026-07-24 | 4548 |
| 2026-07-25 | 4548 |
| 2026-07-28 | 4549 |
| 2026-07-30 | 4549 |
| 2026-08-06 | 4549 |
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# PINTO_model_zoo <p align="center"> <img src="https://user-images.githubusercontent.com/33194443/104581604-2592cb00-56a2-11eb-9610-5eaa0afb6e1f.png" /> </p> [](https://github.com/PINTO0309/PINTO_model_zoo/actions?query=workflow%3ACodeQL) [](https://doi.org/10.5281/zenodo.10229410) [](https://deepwiki.com/PINTO0309/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 <a href="https://github.com/pinto0309/PINTO_model_zoo/graphs/contributors"> <img src="https://contrib.rocks/image?repo=pinto0309/PINTO_model_zoo" /> </a> Made with [contrib.rocks](https://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 - **[[Japanese ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.](https://qiita.com/PINTO/items/008c54536fca690e0572)** - **[[English ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.](https://qiita.com/PINTO/items/865250ee23a15339d556)** - **[Conversion of PyTorch->ONNX->OpenVINO IR model to Tensorflow saved_model / h5 / tflite / pb](https://github.com/PINTO0309/openvino2tensorflow.git)** - **[[English] Converting PyTorch, ONNX, Caffe, and OpenVINO (NCHW) models to Tensorflow / TensorflowLite (NHWC) in a snap - Qiita](https://qiita.com/PINTO/items/ed06e03eb5c007c2e102)** - **[[TF2 Object Detection] Converting SSD models into .tflite uint8 format #9371](https://github.com/tensorflow/models/issues/9371#issuecomment-735252080)** - **[tf.image.resizeを含むFull Integer Quantization (.tflite)モデルのEdgeTPUモデルへの変換後の推論時に発生する "main.ERROR - Only float32 and uint8 are supported currently, got -xxx.Node number n (op name) failed to invoke" エラーの回避方法](https://qiita.com/PINTO/items/6ff62da1d02089442c8c)** - **[A standalone 2MB installer for TensorflowLite v2.4.0-rc4 and a libedgetpu.so.1 build intended to run on a Yocto-generated environment](https://qiita.com/PINTO/items/effb80ee349d8db6af1b)** - **[[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](https://zenn.dev/pinto0309/articles/9d316860f8d418)** - **[Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite.](https://github.com/PINTO0309/tflite2tensorflow)** - **[Add a custom OP to the TFLite runtime to build the wh
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a30fff033c043506, topic:pytorch, topic:tensorflow
matched fp:a30fff033c043506, topic:onnx, readme:inference engine
matched fp:a30fff033c043506, topic:computer-vision, readme:object detection