Bump timm from 0.9.7 to 1.0.7

[dependabot[bot]]

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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Superseded by #899.