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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