resnext101_64x4d.tv_in1k

Resnext101 64X4D

This repository contains LiteRT/TFLite exports of the TIMM image-classification model resnext101_64x4d.tv_in1k.

Model Description

The model files were converted from pretrained TIMM weights published at timm/resnext101_64x4d.tv_in1k.

Available Model Files

Quantization Schema

resnext101_64x4d_int8_channelwise.tflite was quantized with AI Edge Quantizer's static W8A8 recipe (STATIC_WI8_AI8).

The schema is:

Calibration used real ImageNet validation images with the TIMM preprocessing flow for resnext101_64x4d.tv_in1k. The resolved TIMM preprocessing config was {"crop_mode": "center", "crop_pct": 0.875, "input_size": [3, 224, 224], "interpolation": "bilinear", "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225]}. When using APIs that expose raw tensor buffers, prepare the input and output using the quantization parameters stored in the model.

Runtime Compatibility

These artifacts are intended for LiteRT CPU and GPU execution.

The static INT8 channelwise artifact also AOT-compiled successfully through the LiteRT Qualcomm compiler plugin for SM8750, with the compiled model fully selected into Qualcomm NPU dispatch.

Enablement for other NPU backends is still under validation.

Intended Uses & Limitations

The model files were converted from pretrained weights from TIMM. The models may have their own licenses or terms and conditions derived from TIMM and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 83.5
  • GMACs: 15.5
  • Activations (M): 31.2
  • Image size: 224 x 224
• Papers:
  • Aggregated Residual Transformations for Deep Neural Networks: https://arxiv.org/abs/1611.05431
  • Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
• Original: https://github.com/pytorch/vision

Citation

@article{Xie2016,
  title={Aggregated Residual Transformations for Deep Neural Networks},
  author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He},
  journal={arXiv preprint arXiv:1611.05431},
  year={2016}
}

@article{He2015,
  author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  title = {Deep Residual Learning for Image Recognition},
  journal = {arXiv preprint arXiv:1512.03385},
  year = {2015}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}