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.py
timm
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_head
foward_features
, for consistency with CNN models, token selection or pooling now applied in forward_head
timm
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!