densenet161.tv_in1k

Densenet161

This repository contains LiteRT/TFLite exports of the TIMM image-classification model densenet161.tv_in1k.

Model Description

The model files were converted from pretrained TIMM weights published at timm/densenet161.tv_in1k.

Available Model Files

Quantization Schema

densenet161_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 densenet161.tv_in1k. The resolved TIMM preprocessing config was {"crop_mode": "center", "crop_pct": 0.875, "input_size": [3, 224, 224], "interpolation": "bicubic", "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): 28.7
  • GMACs: 7.8
  • Activations (M): 11.1
  • Image size: 224 x 224
• Papers:
  • Densely Connected Convolutional Networks: https://arxiv.org/abs/1608.06993
• Dataset: ImageNet-1k
• Original: https://github.com/pytorch/vision

Citation

@inproceedings{huang2017densely,
  title={Densely Connected Convolutional Networks},
  author={Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q },
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}