darknetaa53 - 79.8 @ 256, 80.5 @ 288convnext_nano - 80.8 @ 224, 81.5 @ 288cs3sedarknet_l - 81.2 @ 256, 81.8 @ 288cs3darknet_x - 81.8 @ 256, 82.2 @ 288cs3sedarknet_x - 82.2 @ 256, 82.7 @ 288cs3edgenet_x - 82.2 @ 256, 82.7 @ 288cs3se_edgenet_x - 82.8 @ 256, 83.5 @ 320cs3* weights above all trained on TPU w/ bits_and_tpu branch. Thanks to TRC program!More models, more fixes
ResNet defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14)CspNet refactored with dataclass config, simplified CrossStage3 (cs3) option. These are closer to YOLO-v5+ backbone defs.srelpos (shared relative position) models trained, and a medium w/ class token.small model. Better than original small, but not their new USI trained weights.resnet10t - 66.5 @ 176, 68.3 @ 224resnet14t - 71.3 @ 176, 72.3 @ 224resnetaa50 - 80.6 @ 224 , 81.6 @ 288darknet53 - 80.0 @ 256, 80.5 @ 288cs3darknet_m - 77.0 @ 256, 77.6 @ 288cs3darknet_focus_m - 76.7 @ 256, 77.3 @ 288cs3darknet_l - 80.4 @ 256, 80.9 @ 288cs3darknet_focus_l - 80.3 @ 256, 80.9 @ 288vit_srelpos_small_patch16_224 - 81.1 @ 224, 82.1 @ 320vit_srelpos_medium_patch16_224 - 82.3 @ 224, 83.1 @ 320vit_relpos_small_patch16_cls_224 - 82.6 @ 224, 83.6 @ 320edgnext_small_rw - 79.6 @ 224, 80.4 @ 320cs3, darknet, and vit_*relpos weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs.timm datasets/parsers. See (https://github.com/rwightman/pytorch-image-models/pull/1274#issuecomment-1178303103)F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2) via LayerNorm2d in all cases. LayerNormExp2d in models/layers/norm.pytimm Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects.vit_relpos_small_patch16_224 - 81.5 @ 224, 82.5 @ 320 — rel pos, layer scale, no class token, avg poolvit_relpos_medium_patch16_rpn_224 - 82.3 @ 224, 83.1 @ 320 — rel pos + res-post-norm, no class token, avg poolvit_relpos_medium_patch16_224 - 82.5 @ 224, 83.3 @ 320 — rel pos, layer scale, no class token, avg poolvit_relpos_base_patch16_gapcls_224 - 82.8 @ 224, 83.9 @ 320 — rel pos, layer scale, class token, avg pool (by mistake)vision_transformer_relpos.py) and Residual Post-Norm branches (from Swin-V2) (vision_transformer*.py)vit_relpos_base_patch32_plus_rpn_256 - 79.5 @ 256, 80.6 @ 320 — rel pos + extended width + res-post-norm, no class token, avg poolvit_relpos_base_patch16_224 - 82.5 @ 224, 83.6 @ 320 — rel pos, layer scale, no class token, avg poolvit_base_patch16_rpn_224 - 82.3 @ 224 — rel pos + res-post-norm, no class token, avg poolHow to Train Your ViT)vit_* models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae).timm models are now officially supported in fast.ai! Just in time for the new Practical Deep Learning course. timmdocs documentation link updated to timm.fast.ai.seresnext101d_32x8d - 83.69 @ 224, 84.35 @ 288seresnextaa101d_32x8d (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288ParallelBlock and LayerScale option to base vit models to support model configs in Three things everyone should know about ViTconvnext_tiny_hnf (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs.norm_norm_norm. IMPORTANT this update for a coming 0.6.x release will likely de-stabilize the master branch for a while. Branch 0.5.x or a previous 0.5.x release can be used if stability is required.regnety_040 - 82.3 @ 224, 82.96 @ 288regnety_064 - 83.0 @ 224, 83.65 @ 288regnety_080 - 83.17 @ 224, 83.86 @ 288regnetv_040 - 82.44 @ 224, 83.18 @ 288 (timm pre-act)regnetv_064 - 83.1 @ 224, 83.71 @ 288 (timm pre-act)regnetz_040 - 83.67 @ 256, 84.25 @ 320regnetz_040h - 83.77 @ 256, 84.5 @ 320 (w/ extra fc in head)resnetv2_50d_gn - 80.8 @ 224, 81.96 @ 288 (pre-act GroupNorm)resnetv2_50d_evos 80.77 @ 224, 82.04 @ 288 (pre-act EvoNormS)regnetz_c16_evos - 81.9 @ 256, 82.64 @ 320 (EvoNormS)regnetz_d8_evos - 83.42 @ 256, 84.04 @ 320 (EvoNormS)xception41p - 82 @ 299 (timm pre-act)xception65 - 83.17 @ 299xception65p - 83.14 @ 299 (timm pre-act)resnext101_64x4d - 82.46 @ 224, 83.16 @ 288seresnext101_32x8d - 83.57 @ 224, 84.270 @ 288resnetrs200 - 83.85 @ 256, 84.44 @ 320forward_head(x, pre_logits=False) fn added to all models to allow separate calls of forward_features + forward_headfoward_features, for consistency with CNN models, token selection or pooling now applied in forward_headtimm on his blog yesterday. Well worth a read. Getting Started with PyTorch Image Models (timm): A Practitioner’s Guidenorm_norm_norm branch back to master (ver 0.6.x) in next week or so.pip install git+https://github.com/rwightman/pytorch-image-models installs!0.5.x releases and a 0.5.x branch will remain stable with a cherry pick or two until dust clears. Recommend sticking to pypi install for a bit if you want stable.mnasnet_small - 65.6 top-1mobilenetv2_050 - 65.9lcnet_100/075/050 - 72.1 / 68.8 / 63.1semnasnet_075 - 73fbnetv3_b/d/g - 79.1 / 79.7 / 82.0eca_halonext26ts - 79.5 @ 256resnet50_gn (new) - 80.1 @ 224, 81.3 @ 288resnet50 - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don’t scale as well to higher res, weights)resnext50_32x4d - 81.1 @ 224, 82.0 @ 288sebotnet33ts_256 (new) - 81.2 @ 224lamhalobotnet50ts_256 - 81.5 @ 256halonet50ts - 81.7 @ 256halo2botnet50ts_256 - 82.0 @ 256resnet101 - 82.0 @ 224, 82.8 @ 288resnetv2_101 (new) - 82.1 @ 224, 83.0 @ 288resnet152 - 82.8 @ 224, 83.5 @ 288regnetz_d8 (new) - 83.5 @ 256, 84.0 @ 320regnetz_e8 (new) - 84.5 @ 256, 85.0 @ 320vit_base_patch8_224 (85.8 top-1) & in21k variant weights added thanks Martins Bruveristimm bits branch).data, a bit more consistency, unit tests for all!