NetEase-GameAI / ResNeSt-caffe

A Caffe version of official PyTorch ResNeSt

ResNeSt-caffe

• A caffe version of official PyTorch-ResNeSt.

• Caffemodels are avaliable here

Model Results

Model Name	Crop Size	PyTorch Top1	Caffe Top1	Caffe Speed
ResNeSt-50	224x224	81.03	81.11	12.9ms
ResNeSt-101	256x256	82.83	83.06	20.9ms
ResNeSt-200	320x320	83.84	84.22	58.0ms
ResNeSt-269	416x416	84.54	84.67	105.2ms

Convert Details

• We convert the official PyTorch-ResNeSt to Caffe by pipeline: PyTorch-ONNX-Caffe.

• For exported ONNX model, we first merge Exp-ReduceSum-Div into one Softmax node. Then we convert to caffe by our onnx2caffe tools written from scratch.

• Caffe models are tested on single GTX-1080Ti. PyTorch results come from official PyTorch-ResNeSt.

  • We first test accuracy on ImageNet2012 val with large batch.

  • Then we test forward time with batch=1 for 10k iterations by evaluation.py tools.

• It seems caffe models are slower than that in ResNeSt-paper

  • Some ops may be more friendly for PyTorch, while less for Caffe.

  • We test on GTX-1080Ti while the latency in paper tested on Tesla-V100.

• Need bvlc-caffe and Permute layer from ssd-caffe.

Evaluation Tools

python evaluation.py -imgs /data/ImageNet2012/val -label /data/ImageNet2012/labels/val.txt -proto resnest50.prototxt -model resnest50.caffemodel -size 224 -batch 20