Model card for regnetz e8.ra3 in1k A RegNetZ image classification model. Trained on ImageNet 1k by Ross Wightman in timm . These RegNetZ B / C / D models explore different group size and layer configurations and did not follow any paper descriptions. Like EfficientNets, this architecture uses linear (non activated) block outputs and an inverted bottleneck (mid block expansion). B16 : ~1.5GF @ 256x256 with a group width of 16. Single layer stem. C16 : ~2.5GF @ 256x256 with a group width of 16. Single layer stem. D32 : ~6GF @ 256x256 with a group width of 32. Tiered 3 layer stem, no pooling. D8 : ~4GF @ 256x256 with a group width of 8. Tiered 3 layer stem, no pooling. E8 : ~10GF @ 256x256 with a group width of 8. Tiered 3 layer stem, no pooling. This model architecture is implemented using timm 's flexible BYOBNet (Bring Your Own Blocks Network). BYOBNet allows configuration of: block / stage layout stem layout output stride (dilation) activation and norm layers channel and spatial / self attention layers ...and also includes timm features common to many other architectures, including: stochastic depth gradient checkpointing layer wise LR decay per stage feature extraction Model Deta…
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