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I ran a few #PyTorch ResNet experiments over the holiday season. Testing my (re)implementation of Google’s AugMix, I trained a vanilla ResNet50 to 78.994% top-1, just ImageNet, 224 resolution. With test time mean-max pooling at 288x288, the same weights manage 80% top-1.
Another result, modifying the ‘deep stem’ from Bag of Tricks. Tiering the stem to (24, 48, 64) or (24, 32, 64) shows benefit over the D stem of (32, 32, 64). 77.6% D vs 78.0% for tiered on an SE-ResNeXt26-32x4d base. Tiered stem inspired by @jeremyphoward
