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qubvel / segmentation_models.pytorch

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
https://smp.readthedocs.io/
MIT License
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Bump timm from 0.9.7 to 1.0.7 #888

Open dependabot[bot] opened 2 weeks ago

dependabot[bot] commented 2 weeks ago

Bumps timm from 0.9.7 to 1.0.7.

Release notes

Sourced from timm's releases.

Release v1.0.7

June 12, 2024

  • MobileNetV4 models and initial set of timm trained weights added:
model top1 top1_err top5 top5_err param_count img_size
mobilenetv4_hybrid_large.e600_r384_in1k 84.266 15.734 96.936 3.064 37.76 448
mobilenetv4_hybrid_large.e600_r384_in1k 83.800 16.200 96.770 3.230 37.76 384
mobilenetv4_conv_large.e600_r384_in1k 83.392 16.608 96.622 3.378 32.59 448
mobilenetv4_conv_large.e600_r384_in1k 82.952 17.048 96.266 3.734 32.59 384
mobilenetv4_conv_large.e500_r256_in1k 82.674 17.326 96.31 3.69 32.59 320
mobilenetv4_conv_large.e500_r256_in1k 81.862 18.138 95.69 4.31 32.59 256
mobilenetv4_hybrid_medium.e500_r224_in1k 81.276 18.724 95.742 4.258 11.07 256
mobilenetv4_conv_medium.e500_r256_in1k 80.858 19.142 95.768 4.232 9.72 320
mobilenetv4_hybrid_medium.e500_r224_in1k 80.442 19.558 95.38 4.62 11.07 224
mobilenetv4_conv_blur_medium.e500_r224_in1k 80.142 19.858 95.298 4.702 9.72 256
mobilenetv4_conv_medium.e500_r256_in1k 79.928 20.072 95.184 4.816 9.72 256
mobilenetv4_conv_medium.e500_r224_in1k 79.808 20.192 95.186 4.814 9.72 256
mobilenetv4_conv_blur_medium.e500_r224_in1k 79.438 20.562 94.932 5.068 9.72 224
mobilenetv4_conv_medium.e500_r224_in1k 79.094 20.906 94.77 5.23 9.72 224
mobilenetv4_conv_small.e2400_r224_in1k 74.616 25.384 92.072 7.928 3.77 256
mobilenetv4_conv_small.e1200_r224_in1k 74.292 25.708 92.116 7.884 3.77 256
mobilenetv4_conv_small.e2400_r224_in1k 73.756 26.244 91.422 8.578 3.77 224
mobilenetv4_conv_small.e1200_r224_in1k 73.454 26.546 91.34 8.66 3.77 224
  • Apple MobileCLIP (https://arxiv.org/pdf/2311.17049, FastViT and ViT-B) image tower model support & weights added (part of OpenCLIP support).
  • ViTamin (https://arxiv.org/abs/2404.02132) CLIP image tower model & weights added (part of OpenCLIP support).
  • OpenAI CLIP Modified ResNet image tower modelling & weight support (via ByobNet). Refactor AttentionPool2d.
  • Refactoring & improvements, especially related to classifier_reset and num_features vs head_hidden_size for forward_features() vs pre_logits

Release v1.0.3

May 14, 2024

  • Support loading PaliGemma jax weights into SigLIP ViT models with average pooling.
  • Add Hiera models from Meta (https://github.com/facebookresearch/hiera).
  • Add normalize= flag for transorms, return non-normalized torch.Tensor with original dytpe (for chug)
  • Version 1.0.3 release

May 11, 2024

  • Searching for Better ViT Baselines (For the GPU Poor) weights and vit variants released. Exploring model shapes between Tiny and Base.
model top1 top5 param_count img_size
vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k 86.202 97.874 64.11 256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k 85.418 97.48 60.4 256
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k 84.322 96.812 63.95 256
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k 83.906 96.684 60.23 256
vit_base_patch16_rope_reg1_gap_256.sbb_in1k 83.866 96.67 86.43 256
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k 83.81 96.824 38.74 256
vit_betwixt_patch16_reg4_gap_256.sbb_in1k 83.706 96.616 60.4 256
vit_betwixt_patch16_reg1_gap_256.sbb_in1k 83.628 96.544 60.4 256

... (truncated)

Commits
  • b28945f Version 1.0.7, prep for release
  • fb13e63 Merge pull request #2203 from huggingface/more_mobile
  • 427b3e4 Update README.md
  • 16e082e Add mobilenetv4 hybrid-large weights
  • e41125c Merge pull request #2209 from huggingface/fcossio-vit-maxpool
  • 6254dfa Add numpy<2.0 to requirements until tests are sorted out for pytorch 2.3 vs o...
  • a224668 Add 2400 epoch mobilenetv4 small weights, almost at paper, rounds to 73.8
  • b1a6f4a Some missed reset_classifier() type annotations
  • 71101eb Refactor vit pooling to add more reduction options, separately callable
  • a0bb5b4 Missing stem_kernel_size argument in EfficientNetFeatures
  • Additional commits viewable in compare view


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