|
|
|
|
|
|
|
|
|
|
|
* Add RDNet ('DenseNets Reloaded', https://arxiv.org/abs/2403.19588), thanks [Donghyun Kim](https://github.com/dhkim0225) |
|
|
|
|
|
|
|
|
* Add `mobilenet_edgetpu_v2_m` weights w/ `ra4` mnv4-small based recipe. 80.1% top-1 @ 224 and 80.7 @ 256. |
|
|
* Release 1.0.8 |
|
|
|
|
|
|
|
|
* More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models |
|
|
|
|
|
| model |top1 |top1_err|top5 |top5_err|param_count|img_size| |
|
|
|--------------------------------------------------------------------------------------------------|------|--------|------|--------|-----------|--------| |
|
|
| [mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k](http://hf.co/timm/mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k)|84.99 |15.01 |97.294|2.706 |32.59 |544 | |
|
|
| [mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k](http://hf.co/timm/mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k)|84.772|15.228 |97.344|2.656 |32.59 |480 | |
|
|
| [mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k](http://hf.co/timm/mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k)|84.64 |15.36 |97.114|2.886 |32.59 |448 | |
|
|
| [mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k](http://hf.co/timm/mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k)|84.314|15.686 |97.102|2.898 |32.59 |384 | |
|
|
| [mobilenetv4_conv_aa_large.e600_r384_in1k](http://hf.co/timm/mobilenetv4_conv_aa_large.e600_r384_in1k) |83.824|16.176 |96.734|3.266 |32.59 |480 | |
|
|
| [mobilenetv4_conv_aa_large.e600_r384_in1k](http://hf.co/timm/mobilenetv4_conv_aa_large.e600_r384_in1k) |83.244|16.756 |96.392|3.608 |32.59 |384 | |
|
|
| [mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k)|82.99 |17.01 |96.67 |3.33 |11.07 |320 | |
|
|
| [mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k)|82.364|17.636 |96.256|3.744 |11.07 |256 | |
|
|
|
|
|
* Impressive MobileNet-V1 and EfficientNet-B0 baseline challenges (https://huggingface.co/blog/rwightman/mobilenet-baselines) |
|
|
|
|
|
| model |top1 |top1_err|top5 |top5_err|param_count|img_size| |
|
|
|--------------------------------------------------------------------------------------------------|------|--------|------|--------|-----------|--------| |
|
|
| [efficientnet_b0.ra4_e3600_r224_in1k](http://hf.co/timm/efficientnet_b0.ra4_e3600_r224_in1k) |79.364|20.636 |94.754|5.246 |5.29 |256 | |
|
|
| [efficientnet_b0.ra4_e3600_r224_in1k](http://hf.co/timm/efficientnet_b0.ra4_e3600_r224_in1k) |78.584|21.416 |94.338|5.662 |5.29 |224 | |
|
|
| [mobilenetv1_100h.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100h.ra4_e3600_r224_in1k) |76.596|23.404 |93.272|6.728 |5.28 |256 | |
|
|
| [mobilenetv1_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k) |76.094|23.906 |93.004|6.996 |4.23 |256 | |
|
|
| [mobilenetv1_100h.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100h.ra4_e3600_r224_in1k) |75.662|24.338 |92.504|7.496 |5.28 |224 | |
|
|
| [mobilenetv1_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k) |75.382|24.618 |92.312|7.688 |4.23 |224 | |
|
|
|
|
|
* Prototype of `set_input_size()` added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation. |
|
|
* Improved support in swin for different size handling, in addition to `set_input_size`, `always_partition` and `strict_img_size` args have been added to `__init__` to allow more flexible input size constraints |
|
|
* Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same. |
|
|
* Add several `tiny` < .5M param models for testing that are actually trained on ImageNet-1k |
|
|
|
|
|
|model |top1 |top1_err|top5 |top5_err|param_count|img_size|crop_pct| |
|
|
|----------------------------|------|--------|------|--------|-----------|--------|--------| |
|
|
|test_efficientnet.r160_in1k |47.156|52.844 |71.726|28.274 |0.36 |192 |1.0 | |
|
|
|test_byobnet.r160_in1k |46.698|53.302 |71.674|28.326 |0.46 |192 |1.0 | |
|
|
|test_efficientnet.r160_in1k |46.426|53.574 |70.928|29.072 |0.36 |160 |0.875 | |
|
|
|test_byobnet.r160_in1k |45.378|54.622 |70.572|29.428 |0.46 |160 |0.875 | |
|
|
|test_vit.r160_in1k|42.0 |58.0 |68.664|31.336 |0.37 |192 |1.0 | |
|
|
|test_vit.r160_in1k|40.822|59.178 |67.212|32.788 |0.37 |160 |0.875 | |
|
|
|
|
|
* Fix vit reg token init, thanks [Promisery](https://github.com/Promisery) |
|
|
* Other misc fixes |
|
|
|
|
|
### June 24, 2024 |
|
|
* 3 more MobileNetV4 hyrid weights with different MQA weight init scheme |
|
|
|
|
|
| model |top1 |top1_err|top5 |top5_err|param_count|img_size| |
|
|
|--------------------------------------------------------------------------------------------------|------|--------|------|--------|-----------|--------| |
|
|
| [mobilenetv4_hybrid_large.ix_e600_r384_in1k](http://hf.co/timm/mobilenetv4_hybrid_large.ix_e600_r384_in1k) |84.356|15.644 |96.892 |3.108 |37.76 |448 | |
|
|
| [mobilenetv4_hybrid_large.ix_e600_r384_in1k](http://hf.co/timm/mobilenetv4_hybrid_large.ix_e600_r384_in1k) |83.990|16.010 |96.702 |3.298 |37.76 |384 | |
|
|
| [mobilenetv4_hybrid_medium.ix_e550_r384_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.ix_e550_r384_in1k) |83.394|16.606 |96.760|3.240 |11.07 |448 | |
|
|
| [mobilenetv4_hybrid_medium.ix_e550_r384_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.ix_e550_r384_in1k) |82.968|17.032 |96.474|3.526 |11.07 |384 | |
|
|
| [mobilenetv4_hybrid_medium.ix_e550_r256_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.ix_e550_r256_in1k) |82.492|17.508 |96.278|3.722 |11.07 |320 | |
|
|
| [mobilenetv4_hybrid_medium.ix_e550_r256_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.ix_e550_r256_in1k) |81.446|18.554 |95.704|4.296 |11.07 |256 | |
|
|
* florence2 weight loading in DaViT model |
|
|
|
|
|
### 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](http://hf.co/timm/mobilenetv4_hybrid_large.e600_r384_in1k) |84.266|15.734 |96.936 |3.064 |37.76 |448 | |
|
|
| [mobilenetv4_hybrid_large.e600_r384_in1k](http://hf.co/timm/mobilenetv4_hybrid_large.e600_r384_in1k) |83.800|16.200 |96.770 |3.230 |37.76 |384 | |
|
|
| [mobilenetv4_conv_large.e600_r384_in1k](http://hf.co/timm/mobilenetv4_conv_large.e600_r384_in1k) |83.392|16.608 |96.622 |3.378 |32.59 |448 | |
|
|
| [mobilenetv4_conv_large.e600_r384_in1k](http://hf.co/timm/mobilenetv4_conv_large.e600_r384_in1k) |82.952|17.048 |96.266 |3.734 |32.59 |384 | |
|
|
| [mobilenetv4_conv_large.e500_r256_in1k](http://hf.co/timm/mobilenetv4_conv_large.e500_r256_in1k) |82.674|17.326 |96.31 |3.69 |32.59 |320 | |
|
|
| [mobilenetv4_conv_large.e500_r256_in1k](http://hf.co/timm/mobilenetv4_conv_large.e500_r256_in1k) |81.862|18.138 |95.69 |4.31 |32.59 |256 | |
|
|
| [mobilenetv4_hybrid_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.e500_r224_in1k) |81.276|18.724 |95.742|4.258 |11.07 |256 | |
|
|
| [mobilenetv4_conv_medium.e500_r256_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r256_in1k) |80.858|19.142 |95.768|4.232 |9.72 |320 | |
|
|
| [mobilenetv4_hybrid_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_hybrid_medium.e500_r224_in1k) |80.442|19.558 |95.38 |4.62 |11.07 |224 | |
|
|
| [mobilenetv4_conv_blur_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_blur_medium.e500_r224_in1k) |80.142|19.858 |95.298|4.702 |9.72 |256 | |
|
|
| [mobilenetv4_conv_medium.e500_r256_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r256_in1k) |79.928|20.072 |95.184|4.816 |9.72 |256 | |
|
|
| [mobilenetv4_conv_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r224_in1k) |79.808|20.192 |95.186|4.814 |9.72 |256 | |
|
|
| [mobilenetv4_conv_blur_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_blur_medium.e500_r224_in1k) |79.438|20.562 |94.932|5.068 |9.72 |224 | |
|
|
| [mobilenetv4_conv_medium.e500_r224_in1k](http://hf.co/timm/mobilenetv4_conv_medium.e500_r224_in1k) |79.094|20.906 |94.77 |5.23 |9.72 |224 | |
|
|
| [mobilenetv4_conv_small.e2400_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e2400_r224_in1k) |74.616|25.384 |92.072|7.928 |3.77 |256 | |
|
|
| [mobilenetv4_conv_small.e1200_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e1200_r224_in1k) |74.292|25.708 |92.116|7.884 |3.77 |256 | |
|
|
| [mobilenetv4_conv_small.e2400_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small.e2400_r224_in1k) |73.756|26.244 |91.422|8.578 |3.77 |224 | |
|
|
| [mobilenetv4_conv_small.e1200_r224_in1k](http://hf.co/timm/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. |
|
|
|
|
|
### 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](https://huggingface.co/timm/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](https://huggingface.co/timm/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](https://huggingface.co/timm/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](https://huggingface.co/timm/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](https://huggingface.co/timm/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](https://huggingface.co/timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k) | 83.81 | 96.824 | 38.74 | 256 | |
|
|
| [vit_betwixt_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in1k) | 83.706 | 96.616 | 60.4 | 256 | |
|
|
| [vit_betwixt_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg1_gap_256.sbb_in1k) | 83.628 | 96.544 | 60.4 | 256 | |
|
|
| [vit_medium_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg4_gap_256.sbb_in1k) | 83.47 | 96.622 | 38.88 | 256 | |
|
|
| [vit_medium_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg1_gap_256.sbb_in1k) | 83.462 | 96.548 | 38.88 | 256 | |
|
|
| [vit_little_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_little_patch16_reg4_gap_256.sbb_in1k) | 82.514 | 96.262 | 22.52 | 256 | |
|
|
| [vit_wee_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_wee_patch16_reg1_gap_256.sbb_in1k) | 80.256 | 95.360 | 13.42 | 256 | |
|
|
| [vit_pwee_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_pwee_patch16_reg1_gap_256.sbb_in1k) | 80.072 | 95.136 | 15.25 | 256 | |
|
|
| [vit_mediumd_patch16_reg4_gap_256.sbb_in12k](https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb_in12k) | N/A | N/A | 64.11 | 256 | |
|
|
| [vit_betwixt_patch16_reg4_gap_256.sbb_in12k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k) | N/A | N/A | 60.4 | 256 | |
|
|
|
|
|
* AttentionExtract helper added to extract attention maps from `timm` models. See example in https://github.com/huggingface/pytorch-image-models/discussions/1232#discussioncomment-9320949 |
|
|
* `forward_intermediates()` API refined and added to more models including some ConvNets that have other extraction methods. |
|
|
* 1017 of 1047 model architectures support `features_only=True` feature extraction. Remaining 34 architectures can be supported but based on priority requests. |
|
|
* Remove torch.jit.script annotated functions including old JIT activations. Conflict with dynamo and dynamo does a much better job when used. |
|
|
|
|
|
### April 11, 2024 |
|
|
* Prepping for a long overdue 1.0 release, things have been stable for a while now. |
|
|
* Significant feature that's been missing for a while, `features_only=True` support for ViT models with flat hidden states or non-std module layouts (so far covering `'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*'`) |
|
|
* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or direclty. |
|
|
```python |
|
|
model = timm.create_model('vit_base_patch16_224') |
|
|
final_feat, intermediates = model.forward_intermediates(input) |
|
|
output = model.forward_head(final_feat) # pooling + classifier head |
|
|
|
|
|
print(final_feat.shape) |
|
|
torch.Size([2, 197, 768]) |
|
|
|
|
|
for f in intermediates: |
|
|
print(f.shape) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
torch.Size([2, 768, 14, 14]) |
|
|
|
|
|
print(output.shape) |
|
|
torch.Size([2, 1000]) |
|
|
``` |
|
|
|
|
|
```python |
|
|
model = timm.create_model('eva02_base_patch16_clip_224', pretrained=True, img_size=512, features_only=True, out_indices=(-3, -2,)) |
|
|
output = model(torch.randn(2, 3, 512, 512)) |
|
|
|
|
|
for o in output: |
|
|
print(o.shape) |
|
|
torch.Size([2, 768, 32, 32]) |
|
|
torch.Size([2, 768, 32, 32]) |
|
|
``` |
|
|
* TinyCLIP vision tower weights added, thx [Thien Tran](https://github.com/gau-nernst) |
|
|
|
|
|
### Feb 19, 2024 |
|
|
* Next-ViT models added. Adapted from https://github.com/bytedance/Next-ViT |
|
|
* HGNet and PP-HGNetV2 models added. Adapted from https://github.com/PaddlePaddle/PaddleClas by [SeeFun](https://github.com/seefun) |
|
|
* Removed setup.py, moved to pyproject.toml based build supported by PDM |
|
|
* Add updated model EMA impl using _for_each for less overhead |
|
|
* Support device args in train script for non GPU devices |
|
|
* Other misc fixes and small additions |
|
|
* Min supported Python version increased to 3.8 |
|
|
* Release 0.9.16 |
|
|
|
|
|
### Jan 8, 2024 |
|
|
Datasets & transform refactoring |
|
|
* HuggingFace streaming (iterable) dataset support (`--dataset hfids:org/dataset`) |
|
|
* Webdataset wrapper tweaks for improved split info fetching, can auto fetch splits from supported HF hub webdataset |
|
|
* Tested HF `datasets` and webdataset wrapper streaming from HF hub with recent `timm` ImageNet uploads to https://huggingface.co/timm |
|
|
* Make input & target column/field keys consistent across datasets and pass via args |
|
|
* Full monochrome support when using e:g: `--input-size 1 224 224` or `--in-chans 1`, sets PIL image conversion appropriately in dataset |
|
|
* Improved several alternate crop & resize transforms (ResizeKeepRatio, RandomCropOrPad, etc) for use in PixParse document AI project |
|
|
* Add SimCLR style color jitter prob along with grayscale and gaussian blur options to augmentations and args |
|
|
* Allow train without validation set (`--val-split ''`) in train script |
|
|
* Add `--bce-sum` (sum over class dim) and `--bce-pos-weight` (positive weighting) args for training as they're common BCE loss tweaks I was often hard coding |
|
|
|
|
|
### Nov 23, 2023 |
|
|
* Added EfficientViT-Large models, thanks [SeeFun](https://github.com/seefun) |
|
|
* Fix Python 3.7 compat, will be dropping support for it soon |
|
|
* Other misc fixes |
|
|
* Release 0.9.12 |
|
|
|
|
|
### Nov 20, 2023 |
|
|
* Added significant flexibility for Hugging Face Hub based timm models via `model_args` config entry. `model_args` will be passed as kwargs through to models on creation. |
|
|
* See example at https://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.json |
|
|
* Usage: https://github.com/huggingface/pytorch-image-models/discussions/2035 |
|
|
* Updated imagenet eval and test set csv files with latest models |
|
|
* `vision_transformer.py` typing and doc cleanup by [Laureηt](https://github.com/Laurent2916) |
|
|
* 0.9.11 release |
|
|
|
|
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### Nov 3, 2023 |
|
|
* [DFN (Data Filtering Networks)](https://huggingface.co/papers/2309.17425) and [MetaCLIP](https://huggingface.co/papers/2309.16671) ViT weights added |
|
|
* DINOv2 'register' ViT model weights added (https://huggingface.co/papers/2309.16588, https://huggingface.co/papers/2304.07193) |
|
|
* Add `quickgelu` ViT variants for OpenAI, DFN, MetaCLIP weights that use it (less efficient) |
|
|
* Improved typing added to ResNet, MobileNet-v3 thanks to [Aryan](https://github.com/a-r-r-o-w) |
|
|
* ImageNet-12k fine-tuned (from LAION-2B CLIP) `convnext_xxlarge` |
|
|
* 0.9.9 release |
|
|
|
|
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### Oct 20, 2023 |
|
|
* [SigLIP](https://huggingface.co/papers/2303.15343) image tower weights supported in `vision_transformer.py`. |
|
|
* Great potential for fine-tune and downstream feature use. |
|
|
* Experimental 'register' support in vit models as per [Vision Transformers Need Registers](https://huggingface.co/papers/2309.16588) |
|
|
* Updated RepViT with new weight release. Thanks [wangao](https://github.com/jameslahm) |
|
|
* Add patch resizing support (on pretrained weight load) to Swin models |
|
|
* 0.9.8 release pending |
|
|
|
|
|
### Sep 1, 2023 |
|
|
* TinyViT added by [SeeFun](https://github.com/seefun) |
|
|
* Fix EfficientViT (MIT) to use torch.autocast so it works back to PT 1.10 |
|
|
* 0.9.7 release |
|
|
|
|
|
### Aug 28, 2023 |
|
|
* Add dynamic img size support to models in `vision_transformer.py`, `vision_transformer_hybrid.py`, `deit.py`, and `eva.py` w/o breaking backward compat. |
|
|
* Add `dynamic_img_size=True` to args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass). |
|
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* Add `dynamic_img_pad=True` to allow image sizes that aren't divisible by patch size (pad bottom right to patch size each forward pass). |
|
|
* Enabling either dynamic mode will break FX tracing unless PatchEmbed module added as leaf. |
|
|
* Existing method of resizing position embedding by passing different `img_size` (interpolate pretrained embed weights once) on creation still works. |
|
|
* Existing method of changing `patch_size` (resize pretrained patch_embed weights once) on creation still works. |
|
|
* Example validation cmd `python validate.py --data-dir /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True` |
|
|
|
|
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### Aug 25, 2023 |
|
|
* Many new models since last release |
|
|
* FastViT - https://arxiv.org/abs/2303.14189 |
|
|
* MobileOne - https://arxiv.org/abs/2206.04040 |
|
|
* InceptionNeXt - https://arxiv.org/abs/2303.16900 |
|
|
* RepGhostNet - https://arxiv.org/abs/2211.06088 (thanks https://github.com/ChengpengChen) |
|
|
* GhostNetV2 - https://arxiv.org/abs/2211.12905 (thanks https://github.com/yehuitang) |
|
|
* EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027 (thanks https://github.com/seefun) |
|
|
* EfficientViT (MIT) - https://arxiv.org/abs/2205.14756 (thanks https://github.com/seefun) |
|
|
* Add `--reparam` arg to `benchmark.py`, `onnx_export.py`, and `validate.py` to trigger layer reparameterization / fusion for models with any one of `reparameterize()`, `switch_to_deploy()` or `fuse()` |
|
|
* Including FastViT, MobileOne, RepGhostNet, EfficientViT (MSRA), RepViT, RepVGG, and LeViT |
|
|
* Preparing 0.9.6 'back to school' release |
|
|
|
|
|
### Aug 11, 2023 |
|
|
* Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights |
|
|
* Example validation cmd to test w/ non-square resize `python validate.py --data-dir /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320` |
|
|
|
|
|
### Aug 3, 2023 |
|
|
* Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted by [SeeFun](https://github.com/seefun) |
|
|
* Fix `selecsls*` model naming regression |
|
|
* Patch and position embedding for ViT/EVA works for bfloat16/float16 weights on load (or activations for on-the-fly resize) |
|
|
* v0.9.5 release prep |
|
|
|
|
|
### July 27, 2023 |
|
|
* Added timm trained `seresnextaa201d_32x8d.sw_in12k_ft_in1k_384` weights (and `.sw_in12k` pretrain) with 87.3% top-1 on ImageNet-1k, best ImageNet ResNet family model I'm aware of. |
|
|
* RepViT model and weights (https://arxiv.org/abs/2307.09283) added by [wangao](https://github.com/jameslahm) |
|
|
* I-JEPA ViT feature weights (no classifier) added by [SeeFun](https://github.com/seefun) |
|
|
* SAM-ViT (segment anything) feature weights (no classifier) added by [SeeFun](https://github.com/seefun) |
|
|
* Add support for alternative feat extraction methods and -ve indices to EfficientNet |
|
|
* Add NAdamW optimizer |
|
|
* Misc fixes |
|
|
|
|
|
### May 11, 2023 |
|
|
* `timm` 0.9 released, transition from 0.8.xdev releases |
|
|
|
|
|
### May 10, 2023 |
|
|
* Hugging Face Hub downloading is now default, 1132 models on https://huggingface.co/timm, 1163 weights in `timm` |
|
|
* DINOv2 vit feature backbone weights added thanks to [Leng Yue](https://github.com/leng-yue) |
|
|
* FB MAE vit feature backbone weights added |
|
|
* OpenCLIP DataComp-XL L/14 feat backbone weights added |
|
|
* MetaFormer (poolformer-v2, caformer, convformer, updated poolformer (v1)) w/ weights added by [Fredo Guan](https://github.com/fffffgggg54) |
|
|
* Experimental `get_intermediate_layers` function on vit/deit models for grabbing hidden states (inspired by DINO impl). This is WIP and may change significantly... feedback welcome. |
|
|
* Model creation throws error if `pretrained=True` and no weights exist (instead of continuing with random initialization) |
|
|
* Fix regression with inception / nasnet TF sourced weights with 1001 classes in original classifiers |
|
|
* bitsandbytes (https://github.com/TimDettmers/bitsandbytes) optimizers added to factory, use `bnb` prefix, ie `bnbadam8bit` |
|
|
* Misc cleanup and fixes |
|
|
* Final testing before switching to a 0.9 and bringing `timm` out of pre-release state |
|
|
|
|
|
### April 27, 2023 |
|
|
* 97% of `timm` models uploaded to HF Hub and almost all updated to support multi-weight pretrained configs |
|
|
* Minor cleanup and refactoring of another batch of models as multi-weight added. More fused_attn (F.sdpa) and features_only support, and torchscript fixes. |
|
|
|
|
|
### April 21, 2023 |
|
|
* Gradient accumulation support added to train script and tested (`--grad-accum-steps`), thanks [Taeksang Kim](https://github.com/voidbag) |
|
|
* More weights on HF Hub (cspnet, cait, volo, xcit, tresnet, hardcorenas, densenet, dpn, vovnet, xception_aligned) |
|
|
* Added `--head-init-scale` and `--head-init-bias` to train.py to scale classiifer head and set fixed bias for fine-tune |
|
|
* Remove all InplaceABN (`inplace_abn`) use, replaced use in tresnet with standard BatchNorm (modified weights accordingly). |
|
|
|
|
|
### April 12, 2023 |
|
|
* Add ONNX export script, validate script, helpers that I've had kicking around for along time. Tweak 'same' padding for better export w/ recent ONNX + pytorch. |
|
|
* Refactor dropout args for vit and vit-like models, separate drop_rate into `drop_rate` (classifier dropout), `proj_drop_rate` (block mlp / out projections), `pos_drop_rate` (position embedding drop), `attn_drop_rate` (attention dropout). Also add patch dropout (FLIP) to vit and eva models. |
|
|
* fused F.scaled_dot_product_attention support to more vit models, add env var (TIMM_FUSED_ATTN) to control, and config interface to enable/disable |
|
|
* Add EVA-CLIP backbones w/ image tower weights, all the way up to 4B param 'enormous' model, and 336x336 OpenAI ViT mode that was missed. |
|
|
|
|
|
### April 5, 2023 |
|
|
* ALL ResNet models pushed to Hugging Face Hub with multi-weight support |
|
|
* All past `timm` trained weights added with recipe based tags to differentiate |
|
|
* All ResNet strikes back A1/A2/A3 (seed 0) and R50 example B/C1/C2/D weights available |
|
|
* Add torchvision v2 recipe weights to existing torchvision originals |
|
|
* See comparison table in https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288#model-comparison |
|
|
* New ImageNet-12k + ImageNet-1k fine-tunes available for a few anti-aliased ResNet models |
|
|
* `resnetaa50d.sw_in12k_ft_in1k` - 81.7 @ 224, 82.6 @ 288 |
|
|
* `resnetaa101d.sw_in12k_ft_in1k` - 83.5 @ 224, 84.1 @ 288 |
|
|
* `seresnextaa101d_32x8d.sw_in12k_ft_in1k` - 86.0 @ 224, 86.5 @ 288 |
|
|
* `seresnextaa101d_32x8d.sw_in12k_ft_in1k_288` - 86.5 @ 288, 86.7 @ 320 |
|
|
|
|
|
### March 31, 2023 |
|
|
* Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models. |
|
|
|
|
|
| model |top1 |top5 |img_size|param_count|gmacs |macts | |
|
|
|----------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------| |
|
|
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45| |
|
|
| convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384 |88.312|98.578|384 |200.13 |101.11|126.74| |
|
|
| convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320 |87.968|98.47 |320 |200.13 |70.21 |88.02 | |
|
|
| convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384 |87.138|98.212|384 |88.59 |45.21 |84.49 | |
|
|
| convnext_base.clip_laion2b_augreg_ft_in12k_in1k |86.344|97.97 |256 |88.59 |20.09 |37.55 | |
|
|
|
|
|
* Add EVA-02 MIM pretrained and fine-tuned weights, push to HF hub and update model cards for all EVA models. First model over 90% top-1 (99% top-5)! Check out the original code & weights at https://github.com/baaivision/EVA for more details on their work blending MIM, CLIP w/ many model, dataset, and train recipe tweaks. |
|
|
|
|
|
| model |top1 |top5 |param_count|img_size| |
|
|
|----------------------------------------------------|------|------|-----------|--------| |
|
|
| [eva02_large_patch14_448.mim_m38m_ft_in22k_in1k](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in1k) |90.054|99.042|305.08 |448 | |
|
|
| eva02_large_patch14_448.mim_in22k_ft_in22k_in1k |89.946|99.01 |305.08 |448 | |
|
|
| eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 | |
|
|
| eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 | |
|
|
| eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 | |
|
|
| eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 | |
|
|
| eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 | |
|
|
| eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 | |
|
|
| eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 | |
|
|
| eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 | |
|
|
| eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 | |
|
|
| eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 | |
|
|
| eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 | |
|
|
| eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 | |
|
|
| eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 | |
|
|
| eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 | |
|
|
|
|
|
* Multi-weight and HF hub for DeiT and MLP-Mixer based models |
|
|
|
|
|
|
|
|
* More weights pushed to HF hub along with multi-weight support, including: `regnet.py`, `rexnet.py`, `byobnet.py`, `resnetv2.py`, `swin_transformer.py`, `swin_transformer_v2.py`, `swin_transformer_v2_cr.py` |
|
|
* Swin Transformer models support feature extraction (NCHW feat maps for `swinv2_cr_*`, and NHWC for all others) and spatial embedding outputs. |
|
|
* FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraint |
|
|
* RegNet weights increased with HF hub push, SWAG, SEER, and torchvision v2 weights. SEER is pretty poor wrt to performance for model size, but possibly useful. |
|
|
* More ImageNet-12k pretrained and 1k fine-tuned `timm` weights: |
|
|
* `rexnetr_200.sw_in12k_ft_in1k` - 82.6 @ 224, 83.2 @ 288 |
|
|
* `rexnetr_300.sw_in12k_ft_in1k` - 84.0 @ 224, 84.5 @ 288 |
|
|
* `regnety_120.sw_in12k_ft_in1k` - 85.0 @ 224, 85.4 @ 288 |
|
|
* `regnety_160.lion_in12k_ft_in1k` - 85.6 @ 224, 86.0 @ 288 |
|
|
* `regnety_160.sw_in12k_ft_in1k` - 85.6 @ 224, 86.0 @ 288 (compare to SWAG PT + 1k FT this is same BUT much lower res, blows SEER FT away) |
|
|
* Model name deprecation + remapping functionality added (a milestone for bringing 0.8.x out of pre-release). Mappings being added... |
|
|
* Minor bug fixes and improvements. |
|
|
|
|
|
|
|
|
* Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) -- see [model card](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup) |
|
|
* Update `convnext_xxlarge` default LayerNorm eps to 1e-5 (for CLIP weights, improved stability) |
|
|
* 0.8.15dev0 |
|
|
|
|
|
|
|
|
* Add 320x320 `convnext_large_mlp.clip_laion2b_ft_320` and `convnext_lage_mlp.clip_laion2b_ft_soup_320` CLIP image tower weights for features & fine-tune |
|
|
* 0.8.13dev0 pypi release for latest changes w/ move to huggingface org |
|
|
|
|
|
|
|
|
* `safetensor` checkpoint support added |
|
|
* Add ideas from 'Scaling Vision Transformers to 22 B. Params' (https://arxiv.org/abs/2302.05442) -- qk norm, RmsNorm, parallel block |
|
|
* Add F.scaled_dot_product_attention support (PyTorch 2.0 only) to `vit_*`, `vit_relpos*`, `coatnet` / `maxxvit` (to start) |
|
|
* Lion optimizer (w/ multi-tensor option) added (https://arxiv.org/abs/2302.06675) |
|
|
* gradient checkpointing works with `features_only=True` |
|
|
|
|
|
|
|
|
* New inference benchmark numbers added in [results](results/) folder. |
|
|
* Add convnext LAION CLIP trained weights and initial set of in1k fine-tunes |
|
|
* `convnext_base.clip_laion2b_augreg_ft_in1k` - 86.2% @ 256x256 |
|
|
* `convnext_base.clip_laiona_augreg_ft_in1k_384` - 86.5% @ 384x384 |
|
|
* `convnext_large_mlp.clip_laion2b_augreg_ft_in1k` - 87.3% @ 256x256 |
|
|
* `convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384` - 87.9% @ 384x384 |
|
|
* Add DaViT models. Supports `features_only=True`. Adapted from https://github.com/dingmyu/davit by [Fredo](https://github.com/fffffgggg54). |
|
|
* Use a common NormMlpClassifierHead across MaxViT, ConvNeXt, DaViT |
|
|
* Add EfficientFormer-V2 model, update EfficientFormer, and refactor LeViT (closely related architectures). Weights on HF hub. |
|
|
* New EfficientFormer-V2 arch, significant refactor from original at (https://github.com/snap-research/EfficientFormer). Supports `features_only=True`. |
|
|
* Minor updates to EfficientFormer. |
|
|
* Refactor LeViT models to stages, add `features_only=True` support to new `conv` variants, weight remap required. |
|
|
* Move ImageNet meta-data (synsets, indices) from `/results` to [`timm/data/_info`](timm/data/_info/). |
|
|
* Add ImageNetInfo / DatasetInfo classes to provide labelling for various ImageNet classifier layouts in `timm` |
|
|
* Update `inference.py` to use, try: `python inference.py --data-dir /folder/to/images --model convnext_small.in12k --label-type detail --topk 5` |
|
|
* Ready for 0.8.10 pypi pre-release (final testing). |
|
|
|
|
|
|
|
|
* Add two convnext 12k -> 1k fine-tunes at 384x384 |
|
|
* `convnext_tiny.in12k_ft_in1k_384` - 85.1 @ 384 |
|
|
* `convnext_small.in12k_ft_in1k_384` - 86.2 @ 384 |
|
|
|
|
|
* Push all MaxxViT weights to HF hub, and add new ImageNet-12k -> 1k fine-tunes for `rw` base MaxViT and CoAtNet 1/2 models |
|
|
|
|
|
|model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |
|
|
|------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |
|
|
|[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| |
|
|
|[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |
|
|
|[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |
|
|
|[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |
|
|
|[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |
|
|
|[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |
|
|
|[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |
|
|
|[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |
|
|
|[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |
|
|
|[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |
|
|
|[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |
|
|
|[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |
|
|
|[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |
|
|
|[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |
|
|
|[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |
|
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|[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |
|
|
|[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |
|
|
|[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |
|
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|[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |
|
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|[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |
|
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|[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |
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|[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |
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|[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |
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|[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |
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|[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |
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|[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |
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|[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |
|
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|[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |
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|[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |
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|[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |
|
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|[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |
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|[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |
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|[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |
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|[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |
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|[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |
|
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|[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |
|
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|[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |
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|[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |
|
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|[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |
|
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|[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |
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|[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |
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|[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |
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|[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| |
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* Update ConvNeXt ImageNet-12k pretrain series w/ two new fine-tuned weights (and pre FT `.in12k` tags) |
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* `convnext_nano.in12k_ft_in1k` - 82.3 @ 224, 82.9 @ 288 (previously released) |
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* `convnext_tiny.in12k_ft_in1k` - 84.2 @ 224, 84.5 @ 288 |
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* `convnext_small.in12k_ft_in1k` - 85.2 @ 224, 85.3 @ 288 |
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* Finally got around to adding `--model-kwargs` and `--opt-kwargs` to scripts to pass through rare args directly to model classes from cmd line |
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* `train.py --data-dir /imagenet --model resnet50 --amp --model-kwargs output_stride=16 act_layer=silu` |
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* `train.py --data-dir /imagenet --model vit_base_patch16_clip_224 --img-size 240 --amp --model-kwargs img_size=240 patch_size=12` |
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* Cleanup some popular models to better support arg passthrough / merge with model configs, more to go. |
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* ConvNeXt-V2 models and weights added to existing `convnext.py` |
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* Paper: [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](http://arxiv.org/abs/2301.00808) |
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* Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC) |
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@dataclass |
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* Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013) |
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* NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP |
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* Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit) |
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* More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use) |
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* More ImageNet-12k (subset of 22k) pretrain models popping up: |
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* `efficientnet_b5.in12k_ft_in1k` - 85.9 @ 448x448 |
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* `vit_medium_patch16_gap_384.in12k_ft_in1k` - 85.5 @ 384x384 |
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* `vit_medium_patch16_gap_256.in12k_ft_in1k` - 84.5 @ 256x256 |
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* `convnext_nano.in12k_ft_in1k` - 82.9 @ 288x288 |
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* Add 'EVA l' to `vision_transformer.py`, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some) |
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* original source: https://github.com/baaivision/EVA |
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| model | top1 | param_count | gmac | macts | hub | |
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|:------------------------------------------|-----:|------------:|------:|------:|:----------------------------------------| |
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| eva_large_patch14_336.in22k_ft_in22k_in1k | 89.2 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_large_patch14_336.in22k_ft_in1k | 88.7 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_large_patch14_196.in22k_ft_in22k_in1k | 88.6 | 304.1 | 61.6 | 63.5 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_large_patch14_196.in22k_ft_in1k | 87.9 | 304.1 | 61.6 | 63.5 | [link](https://huggingface.co/BAAI/EVA) | |
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* Add 'EVA g', BEiT style ViT-g/14 model weights w/ both MIM pretrain and CLIP pretrain to `beit.py`. |
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* original source: https://github.com/baaivision/EVA |
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* paper: https://arxiv.org/abs/2211.07636 |
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| model | top1 | param_count | gmac | macts | hub | |
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|:-----------------------------------------|-------:|--------------:|-------:|--------:|:----------------------------------------| |
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| eva_giant_patch14_560.m30m_ft_in22k_in1k | 89.8 | 1014.4 | 1906.8 | 2577.2 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_giant_patch14_336.m30m_ft_in22k_in1k | 89.6 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_giant_patch14_336.clip_ft_in1k | 89.4 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_giant_patch14_224.clip_ft_in1k | 89.1 | 1012.6 | 267.2 | 192.6 | [link](https://huggingface.co/BAAI/EVA) | |
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* Pre-release (`0.8.0dev0`) of multi-weight support (`model_arch.pretrained_tag`). Install with `pip install --pre timm` |
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* vision_transformer, maxvit, convnext are the first three model impl w/ support |
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* model names are changing with this (previous _21k, etc. fn will merge), still sorting out deprecation handling |
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* bugs are likely, but I need feedback so please try it out |
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* if stability is needed, please use 0.6.x pypi releases or clone from [0.6.x branch](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) |
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* Support for PyTorch 2.0 compile is added in train/validate/inference/benchmark, use `--torchcompile` argument |
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* Inference script allows more control over output, select k for top-class index + prob json, csv or parquet output |
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* Add a full set of fine-tuned CLIP image tower weights from both LAION-2B and original OpenAI CLIP models |
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| model | top1 | param_count | gmac | macts | hub | |
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|:-------------------------------------------------|-------:|--------------:|-------:|--------:|:-------------------------------------------------------------------------------------| |
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| vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k | 88.6 | 632.5 | 391 | 407.5 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k) | |
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| vit_large_patch14_clip_336.openai_ft_in12k_in1k | 88.3 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.openai_ft_in12k_in1k) | |
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| vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k | 88.2 | 632 | 167.4 | 139.4 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k) | |
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| vit_large_patch14_clip_336.laion2b_ft_in12k_in1k | 88.2 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k) | |
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| vit_large_patch14_clip_224.openai_ft_in12k_in1k | 88.2 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in12k_in1k) | |
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| vit_large_patch14_clip_224.laion2b_ft_in12k_in1k | 87.9 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.laion2b_ft_in12k_in1k) | |
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| vit_large_patch14_clip_224.openai_ft_in1k | 87.9 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in1k) | |
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| vit_large_patch14_clip_336.laion2b_ft_in1k | 87.9 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in1k) | |
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| vit_huge_patch14_clip_224.laion2b_ft_in1k | 87.6 | 632 | 167.4 | 139.4 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in1k) | |
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| vit_large_patch14_clip_224.laion2b_ft_in1k | 87.3 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.laion2b_ft_in1k) | |
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| vit_base_patch16_clip_384.laion2b_ft_in12k_in1k | 87.2 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k) | |
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| vit_base_patch16_clip_384.openai_ft_in12k_in1k | 87 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.openai_ft_in12k_in1k) | |
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| vit_base_patch16_clip_384.laion2b_ft_in1k | 86.6 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.laion2b_ft_in1k) | |
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| vit_base_patch16_clip_384.openai_ft_in1k | 86.2 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.openai_ft_in1k) | |
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| vit_base_patch16_clip_224.laion2b_ft_in12k_in1k | 86.2 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.laion2b_ft_in12k_in1k) | |
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| vit_base_patch16_clip_224.openai_ft_in12k_in1k | 85.9 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.openai_ft_in12k_in1k) | |
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| vit_base_patch32_clip_448.laion2b_ft_in12k_in1k | 85.8 | 88.3 | 17.9 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k) | |
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| vit_base_patch16_clip_224.laion2b_ft_in1k | 85.5 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.laion2b_ft_in1k) | |
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| vit_base_patch32_clip_384.laion2b_ft_in12k_in1k | 85.4 | 88.3 | 13.1 | 16.5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_384.laion2b_ft_in12k_in1k) | |
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| vit_base_patch16_clip_224.openai_ft_in1k | 85.3 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.openai_ft_in1k) | |
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| vit_base_patch32_clip_384.openai_ft_in12k_in1k | 85.2 | 88.3 | 13.1 | 16.5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_384.openai_ft_in12k_in1k) | |
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| vit_base_patch32_clip_224.laion2b_ft_in12k_in1k | 83.3 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b_ft_in12k_in1k) | |
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| vit_base_patch32_clip_224.laion2b_ft_in1k | 82.6 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b_ft_in1k) | |
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| vit_base_patch32_clip_224.openai_ft_in1k | 81.9 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.openai_ft_in1k) | |
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* Port of MaxViT Tensorflow Weights from official impl at https://github.com/google-research/maxvit |
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* There was larger than expected drops for the upscaled 384/512 in21k fine-tune weights, possible detail missing, but the 21k FT did seem sensitive to small preprocessing |
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| model | top1 | param_count | gmac | macts | hub | |
|
|
|:-----------------------------------|-------:|--------------:|-------:|--------:|:-----------------------------------------------------------------------| |
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| maxvit_xlarge_tf_512.in21k_ft_in1k | 88.5 | 475.8 | 534.1 | 1413.2 | [link](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) | |
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| maxvit_xlarge_tf_384.in21k_ft_in1k | 88.3 | 475.3 | 292.8 | 668.8 | [link](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) | |
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| maxvit_base_tf_512.in21k_ft_in1k | 88.2 | 119.9 | 138 | 704 | [link](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) | |
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| maxvit_large_tf_512.in21k_ft_in1k | 88 | 212.3 | 244.8 | 942.2 | [link](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) | |
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| maxvit_large_tf_384.in21k_ft_in1k | 88 | 212 | 132.6 | 445.8 | [link](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) | |
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| maxvit_base_tf_384.in21k_ft_in1k | 87.9 | 119.6 | 73.8 | 332.9 | [link](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) | |
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| maxvit_base_tf_512.in1k | 86.6 | 119.9 | 138 | 704 | [link](https://huggingface.co/timm/maxvit_base_tf_512.in1k) | |
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| maxvit_large_tf_512.in1k | 86.5 | 212.3 | 244.8 | 942.2 | [link](https://huggingface.co/timm/maxvit_large_tf_512.in1k) | |
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| maxvit_base_tf_384.in1k | 86.3 | 119.6 | 73.8 | 332.9 | [link](https://huggingface.co/timm/maxvit_base_tf_384.in1k) | |
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| maxvit_large_tf_384.in1k | 86.2 | 212 | 132.6 | 445.8 | [link](https://huggingface.co/timm/maxvit_large_tf_384.in1k) | |
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| maxvit_small_tf_512.in1k | 86.1 | 69.1 | 67.3 | 383.8 | [link](https://huggingface.co/timm/maxvit_small_tf_512.in1k) | |
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| maxvit_tiny_tf_512.in1k | 85.7 | 31 | 33.5 | 257.6 | [link](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) | |
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| maxvit_small_tf_384.in1k | 85.5 | 69 | 35.9 | 183.6 | [link](https://huggingface.co/timm/maxvit_small_tf_384.in1k) | |
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| maxvit_tiny_tf_384.in1k | 85.1 | 31 | 17.5 | 123.4 | [link](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) | |
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| maxvit_large_tf_224.in1k | 84.9 | 211.8 | 43.7 | 127.4 | [link](https://huggingface.co/timm/maxvit_large_tf_224.in1k) | |
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| maxvit_base_tf_224.in1k | 84.9 | 119.5 | 24 | 95 | [link](https://huggingface.co/timm/maxvit_base_tf_224.in1k) | |
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| maxvit_small_tf_224.in1k | 84.4 | 68.9 | 11.7 | 53.2 | [link](https://huggingface.co/timm/maxvit_small_tf_224.in1k) | |
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| maxvit_tiny_tf_224.in1k | 83.4 | 30.9 | 5.6 | 35.8 | [link](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) | |
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* Train and validation script enhancements |
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* Non-GPU (ie CPU) device support |
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* SLURM compatibility for train script |
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* HF datasets support (via ReaderHfds) |
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* TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate) |
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* in_chans !=3 support for scripts / loader |
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* Adan optimizer |
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* Can enable per-step LR scheduling via args |
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* Dataset 'parsers' renamed to 'readers', more descriptive of purpose |
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* AMP args changed, APEX via `--amp-impl apex`, bfloat16 supportedf via `--amp-dtype bfloat16` |
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* main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds |
|
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* master -> main branch rename |
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* More weights in `maxxvit` series, incl first ConvNeXt block based `coatnext` and `maxxvit` experiments: |
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* `coatnext_nano_rw_224` - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm) |
|
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* `maxxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN) |
|
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* `maxvit_rmlp_small_rw_224` - 84.5 @ 224, 85.1 @ 320 (G) |
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* `maxxvit_rmlp_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN) |
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* `coatnet_rmlp_2_rw_224` - 84.6 @ 224, 85 @ 320 (T) |
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* NOTE: official MaxVit weights (in1k) have been released at https://github.com/google-research/maxvit -- some extra work is needed to port and adapt since my impl was created independently of theirs and has a few small differences + the whole TF same padding fun. |
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* LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier) |
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* vit_base_patch32_224_clip_laion2b |
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* vit_large_patch14_224_clip_laion2b |
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* vit_huge_patch14_224_clip_laion2b |
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* vit_giant_patch14_224_clip_laion2b |
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* Hugging Face [`timm` docs](https://huggingface.co/docs/hub/timm) home now exists, look for more here in the future |
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* Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2 |
|
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* Add more weights in `maxxvit` series incl a `pico` (7.5M params, 1.9 GMACs), two `tiny` variants: |
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* `maxvit_rmlp_pico_rw_256` - 80.5 @ 256, 81.3 @ 320 (T) |
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* `maxvit_tiny_rw_224` - 83.5 @ 224 (G) |
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* `maxvit_rmlp_tiny_rw_256` - 84.2 @ 256, 84.8 @ 320 (T) |
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* MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this: |
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* `maxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.6 @ 320 (T) |
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* CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) `timm` original models |
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* both found in [`maxxvit.py`](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/maxxvit.py) model def, contains numerous experiments outside scope of original papers |
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* an unfinished Tensorflow version from MaxVit authors can be found https://github.com/google-research/maxvit |
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* Initial CoAtNet and MaxVit timm pretrained weights (working on more): |
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* `coatnet_nano_rw_224` - 81.7 @ 224 (T) |
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* `coatnet_rmlp_nano_rw_224` - 82.0 @ 224, 82.8 @ 320 (T) |
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* `coatnet_0_rw_224` - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks |
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* `coatnet_bn_0_rw_224` - 82.4 (T) |
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* `maxvit_nano_rw_256` - 82.9 @ 256 (T) |
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* `coatnet_rmlp_1_rw_224` - 83.4 @ 224, 84 @ 320 (T) |
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* `coatnet_1_rw_224` - 83.6 @ 224 (G) |
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* (T) = TPU trained with `bits_and_tpu` branch training code, (G) = GPU trained |
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* GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% `timm` re-write for license purposes) |
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* MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit) |
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* EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer) |
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* PyramidVisionTransformer-V2 (adapted from https://github.com/whai362/PVT) |
|
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* 'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost) |
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* ConvNeXt atto weights added |
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* `convnext_atto` - 75.7 @ 224, 77.0 @ 288 |
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* `convnext_atto_ols` - 75.9 @ 224, 77.2 @ 288 |
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* More custom ConvNeXt smaller model defs with weights |
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* `convnext_femto` - 77.5 @ 224, 78.7 @ 288 |
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* `convnext_femto_ols` - 77.9 @ 224, 78.9 @ 288 |
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* `convnext_pico` - 79.5 @ 224, 80.4 @ 288 |
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* `convnext_pico_ols` - 79.5 @ 224, 80.5 @ 288 |
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* `convnext_nano_ols` - 80.9 @ 224, 81.6 @ 288 |
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* Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt) |
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* Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks [Hugo Touvron](https://github.com/TouvronHugo)! |
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* All runtime benchmark and validation result csv files are finally up-to-date! |
|
|
* A few more weights & model defs added: |
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|
* `darknetaa53` - 79.8 @ 256, 80.5 @ 288 |
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|
* `convnext_nano` - 80.8 @ 224, 81.5 @ 288 |
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* `cs3sedarknet_l` - 81.2 @ 256, 81.8 @ 288 |
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* `cs3darknet_x` - 81.8 @ 256, 82.2 @ 288 |
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* `cs3sedarknet_x` - 82.2 @ 256, 82.7 @ 288 |
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* `cs3edgenet_x` - 82.2 @ 256, 82.7 @ 288 |
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* `cs3se_edgenet_x` - 82.8 @ 256, 83.5 @ 320 |
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|
* `cs3*` weights above all trained on TPU w/ `bits_and_tpu` branch. Thanks to TRC program! |
|
|
* Add output_stride=8 and 16 support to ConvNeXt (dilation) |
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|
* deit3 models not being able to resize pos_emb fixed |
|
|
* Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5) |
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More models, more fixes |
|
|
* Official research models (w/ weights) added: |
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* EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt) |
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|
* MobileViT-V2 from (https://github.com/apple/ml-cvnets) |
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|
* DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit) |
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|
* My own models: |
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|
* Small `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. |
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|
* More relative position vit fiddling. Two `srelpos` (shared relative position) models trained, and a medium w/ class token. |
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|
* Add an alternate downsample mode to EdgeNeXt and train a `small` model. Better than original small, but not their new USI trained weights. |
|
|
* My own model weight results (all ImageNet-1k training) |
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|
* `resnet10t` - 66.5 @ 176, 68.3 @ 224 |
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|
* `resnet14t` - 71.3 @ 176, 72.3 @ 224 |
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* `resnetaa50` - 80.6 @ 224 , 81.6 @ 288 |
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|
* `darknet53` - 80.0 @ 256, 80.5 @ 288 |
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* `cs3darknet_m` - 77.0 @ 256, 77.6 @ 288 |
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* `cs3darknet_focus_m` - 76.7 @ 256, 77.3 @ 288 |
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* `cs3darknet_l` - 80.4 @ 256, 80.9 @ 288 |
|
|
* `cs3darknet_focus_l` - 80.3 @ 256, 80.9 @ 288 |
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* `vit_srelpos_small_patch16_224` - 81.1 @ 224, 82.1 @ 320 |
|
|
* `vit_srelpos_medium_patch16_224` - 82.3 @ 224, 83.1 @ 320 |
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|
* `vit_relpos_small_patch16_cls_224` - 82.6 @ 224, 83.6 @ 320 |
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* `edgnext_small_rw` - 79.6 @ 224, 80.4 @ 320 |
|
|
* `cs3`, `darknet`, and `vit_*relpos` weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs. |
|
|
* Hugging Face Hub support fixes verified, demo notebook TBA |
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|
* Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation. |
|
|
* Add support to change image extensions scanned by `timm` datasets/readers. See (https://github.com/rwightman/pytorch-image-models/pull/1274 |
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|
* Default ConvNeXt LayerNorm impl to use `F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2)` via `LayerNorm2d` in all cases. |
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|
* a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges. |
|
|
* previous impl exists as `LayerNormExp2d` in `models/layers/norm.py` |
|
|
* Numerous bug fixes |
|
|
* Currently testing for imminent PyPi 0.6.x release |
|
|
* LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)? |
|
|
* ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ... |
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* Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript. |
|
|
* Some refactoring for existing `timm` Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects. |
|
|
* More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program) |
|
|
* `vit_relpos_small_patch16_224` - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool |
|
|
* `vit_relpos_medium_patch16_rpn_224` - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool |
|
|
* `vit_relpos_medium_patch16_224` - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool |
|
|
* `vit_relpos_base_patch16_gapcls_224` - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake) |
|
|
* Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020) |
|
|
* Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials |
|
|
* Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg) |
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|
|
* Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (`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 pool |
|
|
* `vit_relpos_base_patch16_224` - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool |
|
|
* `vit_base_patch16_rpn_224` - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool |
|
|
* Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie `How to Train Your ViT`) |
|
|
* `vit_*` models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae). |
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|
|
* `timm` models are now officially supported in [fast.ai](https://www.fast.ai/)! Just in time for the new Practical Deep Learning course. `timmdocs` documentation link updated to [timm.fast.ai](http://timm.fast.ai/). |
|
|
* Two more model weights added in the TPU trained [series](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights). Some In22k pretrain still in progress. |
|
|
* `seresnext101d_32x8d` - 83.69 @ 224, 84.35 @ 288 |
|
|
* `seresnextaa101d_32x8d` (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288 |
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|
|
|
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|
|
* Add `ParallelBlock` and `LayerScale` option to base vit models to support model configs in [Three things everyone should know about ViT](https://arxiv.org/abs/2203.09795) |
|
|
* `convnext_tiny_hnf` (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs. |
|
|
|
|
|
|
|
|
* Merge `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`](https://github.com/rwightman/pytorch-image-models/tree/0.5.x) or a previous 0.5.x release can be used if stability is required. |
|
|
* Significant weights update (all TPU trained) as described in this [release](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights) |
|
|
* `regnety_040` - 82.3 @ 224, 82.96 @ 288 |
|
|
* `regnety_064` - 83.0 @ 224, 83.65 @ 288 |
|
|
* `regnety_080` - 83.17 @ 224, 83.86 @ 288 |
|
|
* `regnetv_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 @ 320 |
|
|
* `regnetz_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 @ 299 |
|
|
* `xception65p` - 83.14 @ 299 (timm pre-act) |
|
|
* `resnext101_64x4d` - 82.46 @ 224, 83.16 @ 288 |
|
|
* `seresnext101_32x8d` - 83.57 @ 224, 84.270 @ 288 |
|
|
* `resnetrs200` - 83.85 @ 256, 84.44 @ 320 |
|
|
* HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon) |
|
|
* SwinTransformer-V2 implementation added. Submitted by [Christoph Reich](https://github.com/ChristophReich1996). Training experiments and model changes by myself are ongoing so expect compat breaks. |
|
|
* Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2 |
|
|
* MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets |
|
|
* PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer |
|
|
* VOLO models w/ weights adapted from https://github.com/sail-sg/volo |
|
|
* Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc |
|
|
* Enhance support for alternate norm + act ('NormAct') layers added to a number of models, esp EfficientNet/MobileNetV3, RegNet, and aligned Xception |
|
|
* Grouped conv support added to EfficientNet family |
|
|
* Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler |
|
|
* Gradient checkpointing support added to many models |
|
|
* `forward_head(x, pre_logits=False)` fn added to all models to allow separate calls of `forward_features` + `forward_head` |
|
|
* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `foward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head` |
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|
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|
|
* [Chris Hughes](https://github.com/Chris-hughes10) posted an exhaustive run through of `timm` on his blog yesterday. Well worth a read. [Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055) |
|
|
* I'm currently prepping to merge the `norm_norm_norm` branch back to master (ver 0.6.x) in next week or so. |
|
|
* The changes are more extensive than usual and may destabilize and break some model API use (aiming for full backwards compat). So, beware `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. |
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|
|
* Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon.... |
|
|
* Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features |
|
|
* Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way... |
|
|
* `mnasnet_small` - 65.6 top-1 |
|
|
* `mobilenetv2_050` - 65.9 |
|
|
* `lcnet_100/075/050` - 72.1 / 68.8 / 63.1 |
|
|
* `semnasnet_075` - 73 |
|
|
* `fbnetv3_b/d/g` - 79.1 / 79.7 / 82.0 |
|
|
* TinyNet models added by [rsomani95](https://github.com/rsomani95) |
|
|
* LCNet added via MobileNetV3 architecture |
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|
|
* ConvNeXt-V2 models and weights added to existing `convnext.py` |
|
|
* Paper: [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](http://arxiv.org/abs/2301.00808) |
|
|
* Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC) |
|
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|
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|
|
* Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013) |
|
|
* NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP |
|
|
* Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit) |
|
|
* More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use) |
|
|
* More ImageNet-12k (subset of 22k) pretrain models popping up: |
|
|
* `efficientnet_b5.in12k_ft_in1k` - 85.9 @ 448x448 |
|
|
* `vit_medium_patch16_gap_384.in12k_ft_in1k` - 85.5 @ 384x384 |
|
|
* `vit_medium_patch16_gap_256.in12k_ft_in1k` - 84.5 @ 256x256 |
|
|
* `convnext_nano.in12k_ft_in1k` - 82.9 @ 288x288 |
|
|
|
|
|
|
|
|
* Add 'EVA l' to `vision_transformer.py`, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some) |
|
|
* original source: https://github.com/baaivision/EVA |
|
|
|
|
|
| model | top1 | param_count | gmac | macts | hub | |
|
|
|:------------------------------------------|-----:|------------:|------:|------:|:----------------------------------------| |
|
|
| eva_large_patch14_336.in22k_ft_in22k_in1k | 89.2 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/BAAI/EVA) | |
|
|
| eva_large_patch14_336.in22k_ft_in1k | 88.7 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/BAAI/EVA) | |
|
|
| eva_large_patch14_196.in22k_ft_in22k_in1k | 88.6 | 304.1 | 61.6 | 63.5 | [link](https://huggingface.co/BAAI/EVA) | |
|
|
| eva_large_patch14_196.in22k_ft_in1k | 87.9 | 304.1 | 61.6 | 63.5 | [link](https://huggingface.co/BAAI/EVA) | |
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|
|
|
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|
|
* Add 'EVA g', BEiT style ViT-g/14 model weights w/ both MIM pretrain and CLIP pretrain to `beit.py`. |
|
|
* original source: https://github.com/baaivision/EVA |
|
|
* paper: https://arxiv.org/abs/2211.07636 |
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|
|
|
|
| model | top1 | param_count | gmac | macts | hub | |
|
|
|:-----------------------------------------|-------:|--------------:|-------:|--------:|:----------------------------------------| |
|
|
| eva_giant_patch14_560.m30m_ft_in22k_in1k | 89.8 | 1014.4 | 1906.8 | 2577.2 | [link](https://huggingface.co/BAAI/EVA) | |
|
|
| eva_giant_patch14_336.m30m_ft_in22k_in1k | 89.6 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_giant_patch14_336.clip_ft_in1k | 89.4 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | |
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| eva_giant_patch14_224.clip_ft_in1k | 89.1 | 1012.6 | 267.2 | 192.6 | [link](https://huggingface.co/BAAI/EVA) | |
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* Pre-release (`0.8.0dev0`) of multi-weight support (`model_arch.pretrained_tag`). Install with `pip install --pre timm` |
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* vision_transformer, maxvit, convnext are the first three model impl w/ support |
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* model names are changing with this (previous _21k, etc. fn will merge), still sorting out deprecation handling |
|
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* bugs are likely, but I need feedback so please try it out |
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* if stability is needed, please use 0.6.x pypi releases or clone from [0.6.x branch](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) |
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* Support for PyTorch 2.0 compile is added in train/validate/inference/benchmark, use `--torchcompile` argument |
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* Inference script allows more control over output, select k for top-class index + prob json, csv or parquet output |
|
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* Add a full set of fine-tuned CLIP image tower weights from both LAION-2B and original OpenAI CLIP models |
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| model | top1 | param_count | gmac | macts | hub | |
|
|
|:-------------------------------------------------|-------:|--------------:|-------:|--------:|:-------------------------------------------------------------------------------------| |
|
|
| vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k | 88.6 | 632.5 | 391 | 407.5 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k) | |
|
|
| vit_large_patch14_clip_336.openai_ft_in12k_in1k | 88.3 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.openai_ft_in12k_in1k) | |
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|
| vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k | 88.2 | 632 | 167.4 | 139.4 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k) | |
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| vit_large_patch14_clip_336.laion2b_ft_in12k_in1k | 88.2 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k) | |
|
|
| vit_large_patch14_clip_224.openai_ft_in12k_in1k | 88.2 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in12k_in1k) | |
|
|
| vit_large_patch14_clip_224.laion2b_ft_in12k_in1k | 87.9 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.laion2b_ft_in12k_in1k) | |
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| vit_large_patch14_clip_224.openai_ft_in1k | 87.9 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in1k) | |
|
|
| vit_large_patch14_clip_336.laion2b_ft_in1k | 87.9 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in1k) | |
|
|
| vit_huge_patch14_clip_224.laion2b_ft_in1k | 87.6 | 632 | 167.4 | 139.4 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in1k) | |
|
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| vit_large_patch14_clip_224.laion2b_ft_in1k | 87.3 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.laion2b_ft_in1k) | |
|
|
| vit_base_patch16_clip_384.laion2b_ft_in12k_in1k | 87.2 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k) | |
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|
| vit_base_patch16_clip_384.openai_ft_in12k_in1k | 87 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.openai_ft_in12k_in1k) | |
|
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| vit_base_patch16_clip_384.laion2b_ft_in1k | 86.6 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.laion2b_ft_in1k) | |
|
|
| vit_base_patch16_clip_384.openai_ft_in1k | 86.2 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.openai_ft_in1k) | |
|
|
| vit_base_patch16_clip_224.laion2b_ft_in12k_in1k | 86.2 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.laion2b_ft_in12k_in1k) | |
|
|
| vit_base_patch16_clip_224.openai_ft_in12k_in1k | 85.9 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.openai_ft_in12k_in1k) | |
|
|
| vit_base_patch32_clip_448.laion2b_ft_in12k_in1k | 85.8 | 88.3 | 17.9 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k) | |
|
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| vit_base_patch16_clip_224.laion2b_ft_in1k | 85.5 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.laion2b_ft_in1k) | |
|
|
| vit_base_patch32_clip_384.laion2b_ft_in12k_in1k | 85.4 | 88.3 | 13.1 | 16.5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_384.laion2b_ft_in12k_in1k) | |
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| vit_base_patch16_clip_224.openai_ft_in1k | 85.3 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.openai_ft_in1k) | |
|
|
| vit_base_patch32_clip_384.openai_ft_in12k_in1k | 85.2 | 88.3 | 13.1 | 16.5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_384.openai_ft_in12k_in1k) | |
|
|
| vit_base_patch32_clip_224.laion2b_ft_in12k_in1k | 83.3 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b_ft_in12k_in1k) | |
|
|
| vit_base_patch32_clip_224.laion2b_ft_in1k | 82.6 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b_ft_in1k) | |
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|
| vit_base_patch32_clip_224.openai_ft_in1k | 81.9 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.openai_ft_in1k) | |
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* Port of MaxViT Tensorflow Weights from official impl at https://github.com/google-research/maxvit |
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* There was larger than expected drops for the upscaled 384/512 in21k fine-tune weights, possible detail missing, but the 21k FT did seem sensitive to small preprocessing |
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|
|
| model | top1 | param_count | gmac | macts | hub | |
|
|
|:-----------------------------------|-------:|--------------:|-------:|--------:|:-----------------------------------------------------------------------| |
|
|
| maxvit_xlarge_tf_512.in21k_ft_in1k | 88.5 | 475.8 | 534.1 | 1413.2 | [link](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) | |
|
|
| maxvit_xlarge_tf_384.in21k_ft_in1k | 88.3 | 475.3 | 292.8 | 668.8 | [link](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) | |
|
|
| maxvit_base_tf_512.in21k_ft_in1k | 88.2 | 119.9 | 138 | 704 | [link](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) | |
|
|
| maxvit_large_tf_512.in21k_ft_in1k | 88 | 212.3 | 244.8 | 942.2 | [link](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) | |
|
|
| maxvit_large_tf_384.in21k_ft_in1k | 88 | 212 | 132.6 | 445.8 | [link](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) | |
|
|
| maxvit_base_tf_384.in21k_ft_in1k | 87.9 | 119.6 | 73.8 | 332.9 | [link](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) | |
|
|
| maxvit_base_tf_512.in1k | 86.6 | 119.9 | 138 | 704 | [link](https://huggingface.co/timm/maxvit_base_tf_512.in1k) | |
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|
| maxvit_large_tf_512.in1k | 86.5 | 212.3 | 244.8 | 942.2 | [link](https://huggingface.co/timm/maxvit_large_tf_512.in1k) | |
|
|
| maxvit_base_tf_384.in1k | 86.3 | 119.6 | 73.8 | 332.9 | [link](https://huggingface.co/timm/maxvit_base_tf_384.in1k) | |
|
|
| maxvit_large_tf_384.in1k | 86.2 | 212 | 132.6 | 445.8 | [link](https://huggingface.co/timm/maxvit_large_tf_384.in1k) | |
|
|
| maxvit_small_tf_512.in1k | 86.1 | 69.1 | 67.3 | 383.8 | [link](https://huggingface.co/timm/maxvit_small_tf_512.in1k) | |
|
|
| maxvit_tiny_tf_512.in1k | 85.7 | 31 | 33.5 | 257.6 | [link](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) | |
|
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| maxvit_small_tf_384.in1k | 85.5 | 69 | 35.9 | 183.6 | [link](https://huggingface.co/timm/maxvit_small_tf_384.in1k) | |
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| maxvit_tiny_tf_384.in1k | 85.1 | 31 | 17.5 | 123.4 | [link](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) | |
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|
| maxvit_large_tf_224.in1k | 84.9 | 211.8 | 43.7 | 127.4 | [link](https://huggingface.co/timm/maxvit_large_tf_224.in1k) | |
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| maxvit_base_tf_224.in1k | 84.9 | 119.5 | 24 | 95 | [link](https://huggingface.co/timm/maxvit_base_tf_224.in1k) | |
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| maxvit_small_tf_224.in1k | 84.4 | 68.9 | 11.7 | 53.2 | [link](https://huggingface.co/timm/maxvit_small_tf_224.in1k) | |
|
|
| maxvit_tiny_tf_224.in1k | 83.4 | 30.9 | 5.6 | 35.8 | [link](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) | |
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* Train and validation script enhancements |
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|
* Non-GPU (ie CPU) device support |
|
|
* SLURM compatibility for train script |
|
|
* HF datasets support (via ReaderHfds) |
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* TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate) |
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* in_chans !=3 support for scripts / loader |
|
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* Adan optimizer |
|
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* Can enable per-step LR scheduling via args |
|
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* Dataset 'parsers' renamed to 'readers', more descriptive of purpose |
|
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* AMP args changed, APEX via `--amp-impl apex`, bfloat16 supportedf via `--amp-dtype bfloat16` |
|
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* main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds |
|
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* master -> main branch rename |
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* More weights in `maxxvit` series, incl first ConvNeXt block based `coatnext` and `maxxvit` experiments: |
|
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* `coatnext_nano_rw_224` - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm) |
|
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* `maxxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN) |
|
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* `maxvit_rmlp_small_rw_224` - 84.5 @ 224, 85.1 @ 320 (G) |
|
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* `maxxvit_rmlp_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN) |
|
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* `coatnet_rmlp_2_rw_224` - 84.6 @ 224, 85 @ 320 (T) |
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* NOTE: official MaxVit weights (in1k) have been released at https://github.com/google-research/maxvit -- some extra work is needed to port and adapt since my impl was created independently of theirs and has a few small differences + the whole TF same padding fun. |
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* LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier) |
|
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* vit_base_patch32_224_clip_laion2b |
|
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* vit_large_patch14_224_clip_laion2b |
|
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* vit_huge_patch14_224_clip_laion2b |
|
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* vit_giant_patch14_224_clip_laion2b |
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* Hugging Face [`timm` docs](https://huggingface.co/docs/hub/timm) home now exists, look for more here in the future |
|
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* Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2 |
|
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* Add more weights in `maxxvit` series incl a `pico` (7.5M params, 1.9 GMACs), two `tiny` variants: |
|
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* `maxvit_rmlp_pico_rw_256` - 80.5 @ 256, 81.3 @ 320 (T) |
|
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* `maxvit_tiny_rw_224` - 83.5 @ 224 (G) |
|
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* `maxvit_rmlp_tiny_rw_256` - 84.2 @ 256, 84.8 @ 320 (T) |
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|
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* MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this: |
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* `maxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.6 @ 320 (T) |
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* CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) `timm` original models |
|
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* both found in [`maxxvit.py`](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/maxxvit.py) model def, contains numerous experiments outside scope of original papers |
|
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* an unfinished Tensorflow version from MaxVit authors can be found https://github.com/google-research/maxvit |
|
|
* Initial CoAtNet and MaxVit timm pretrained weights (working on more): |
|
|
* `coatnet_nano_rw_224` - 81.7 @ 224 (T) |
|
|
* `coatnet_rmlp_nano_rw_224` - 82.0 @ 224, 82.8 @ 320 (T) |
|
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* `coatnet_0_rw_224` - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks |
|
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* `coatnet_bn_0_rw_224` - 82.4 (T) |
|
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* `maxvit_nano_rw_256` - 82.9 @ 256 (T) |
|
|
* `coatnet_rmlp_1_rw_224` - 83.4 @ 224, 84 @ 320 (T) |
|
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* `coatnet_1_rw_224` - 83.6 @ 224 (G) |
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|
* (T) = TPU trained with `bits_and_tpu` branch training code, (G) = GPU trained |
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* GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% `timm` re-write for license purposes) |
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* MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit) |
|
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* EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer) |
|
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* PyramidVisionTransformer-V2 (adapted from https://github.com/whai362/PVT) |
|
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* 'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost) |
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* ConvNeXt atto weights added |
|
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* `convnext_atto` - 75.7 @ 224, 77.0 @ 288 |
|
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* `convnext_atto_ols` - 75.9 @ 224, 77.2 @ 288 |
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* More custom ConvNeXt smaller model defs with weights |
|
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* `convnext_femto` - 77.5 @ 224, 78.7 @ 288 |
|
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* `convnext_femto_ols` - 77.9 @ 224, 78.9 @ 288 |
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* `convnext_pico` - 79.5 @ 224, 80.4 @ 288 |
|
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* `convnext_pico_ols` - 79.5 @ 224, 80.5 @ 288 |
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* `convnext_nano_ols` - 80.9 @ 224, 81.6 @ 288 |
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* Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt) |
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* Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks [Hugo Touvron](https://github.com/TouvronHugo)! |
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* All runtime benchmark and validation result csv files are up-to-date! |
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|
* A few more weights & model defs added: |
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* `darknetaa53` - 79.8 @ 256, 80.5 @ 288 |
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* `convnext_nano` - 80.8 @ 224, 81.5 @ 288 |
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* `cs3sedarknet_l` - 81.2 @ 256, 81.8 @ 288 |
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* `cs3darknet_x` - 81.8 @ 256, 82.2 @ 288 |
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* `cs3sedarknet_x` - 82.2 @ 256, 82.7 @ 288 |
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* `cs3edgenet_x` - 82.2 @ 256, 82.7 @ 288 |
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* `cs3se_edgenet_x` - 82.8 @ 256, 83.5 @ 320 |
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* `cs3*` weights above all trained on TPU w/ `bits_and_tpu` branch. Thanks to TRC program! |
|
|
* Add output_stride=8 and 16 support to ConvNeXt (dilation) |
|
|
* deit3 models not being able to resize pos_emb fixed |
|
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* Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5) |
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More models, more fixes |
|
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* Official research models (w/ weights) added: |
|
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* EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt) |
|
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* MobileViT-V2 from (https://github.com/apple/ml-cvnets) |
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* DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit) |
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* My own models: |
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* Small `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. |
|
|
* More relative position vit fiddling. Two `srelpos` (shared relative position) models trained, and a medium w/ class token. |
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* Add an alternate downsample mode to EdgeNeXt and train a `small` model. Better than original small, but not their new USI trained weights. |
|
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* My own model weight results (all ImageNet-1k training) |
|
|
* `resnet10t` - 66.5 @ 176, 68.3 @ 224 |
|
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* `resnet14t` - 71.3 @ 176, 72.3 @ 224 |
|
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* `resnetaa50` - 80.6 @ 224 , 81.6 @ 288 |
|
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* `darknet53` - 80.0 @ 256, 80.5 @ 288 |
|
|
* `cs3darknet_m` - 77.0 @ 256, 77.6 @ 288 |
|
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* `cs3darknet_focus_m` - 76.7 @ 256, 77.3 @ 288 |
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* `cs3darknet_l` - 80.4 @ 256, 80.9 @ 288 |
|
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* `cs3darknet_focus_l` - 80.3 @ 256, 80.9 @ 288 |
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* `vit_srelpos_small_patch16_224` - 81.1 @ 224, 82.1 @ 320 |
|
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* `vit_srelpos_medium_patch16_224` - 82.3 @ 224, 83.1 @ 320 |
|
|
* `vit_relpos_small_patch16_cls_224` - 82.6 @ 224, 83.6 @ 320 |
|
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* `edgnext_small_rw` - 79.6 @ 224, 80.4 @ 320 |
|
|
* `cs3`, `darknet`, and `vit_*relpos` weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs. |
|
|
* Hugging Face Hub support fixes verified, demo notebook TBA |
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|
* Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation. |
|
|
* Add support to change image extensions scanned by `timm` datasets/parsers. See (https://github.com/rwightman/pytorch-image-models/pull/1274 |
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* Default ConvNeXt LayerNorm impl to use `F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2)` via `LayerNorm2d` in all cases. |
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|
* a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges. |
|
|
* previous impl exists as `LayerNormExp2d` in `models/layers/norm.py` |
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* Numerous bug fixes |
|
|
* Currently testing for imminent PyPi 0.6.x release |
|
|
* LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)? |
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* ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ... |
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* Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript. |
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* Some refactoring for existing `timm` Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects. |
|
|
* More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program) |
|
|
* `vit_relpos_small_patch16_224` - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool |
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* `vit_relpos_medium_patch16_rpn_224` - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool |
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* `vit_relpos_medium_patch16_224` - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool |
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* `vit_relpos_base_patch16_gapcls_224` - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake) |
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* Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020) |
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* Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials |
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* Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg) |
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* Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (`vision_transformer_relpos.py`) and Residual Post-Norm branches (from Swin-V2) (`vision_transformer*.py`) |
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* `vit_relpos_base_patch32_plus_rpn_256` - 79.5 @ 256, 80.6 @ 320 -- rel pos + extended width + res-post-norm, no class token, avg pool |
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* `vit_relpos_base_patch16_224` - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool |
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* `vit_base_patch16_rpn_224` - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool |
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* Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie `How to Train Your ViT`) |
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* `vit_*` models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae). |
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* `timm` models are now officially supported in [fast.ai](https://www.fast.ai/)! Just in time for the new Practical Deep Learning course. `timmdocs` documentation link updated to [timm.fast.ai](http://timm.fast.ai/). |
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* Two more model weights added in the TPU trained [series](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights). Some In22k pretrain still in progress. |
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* `seresnext101d_32x8d` - 83.69 @ 224, 84.35 @ 288 |
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* `seresnextaa101d_32x8d` (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288 |
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* Add `ParallelBlock` and `LayerScale` option to base vit models to support model configs in [Three things everyone should know about ViT](https://arxiv.org/abs/2203.09795) |
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* `convnext_tiny_hnf` (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs. |
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* Merge `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`](https://github.com/rwightman/pytorch-image-models/tree/0.5.x) or a previous 0.5.x release can be used if stability is required. |
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* Significant weights update (all TPU trained) as described in this [release](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights) |
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* `regnety_040` - 82.3 @ 224, 82.96 @ 288 |
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* `regnety_064` - 83.0 @ 224, 83.65 @ 288 |
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* `regnety_080` - 83.17 @ 224, 83.86 @ 288 |
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* `regnetv_040` - 82.44 @ 224, 83.18 @ 288 (timm pre-act) |
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* `regnetv_064` - 83.1 @ 224, 83.71 @ 288 (timm pre-act) |
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* `regnetz_040` - 83.67 @ 256, 84.25 @ 320 |
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* `regnetz_040h` - 83.77 @ 256, 84.5 @ 320 (w/ extra fc in head) |
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* `resnetv2_50d_gn` - 80.8 @ 224, 81.96 @ 288 (pre-act GroupNorm) |
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* `resnetv2_50d_evos` 80.77 @ 224, 82.04 @ 288 (pre-act EvoNormS) |
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* `regnetz_c16_evos` - 81.9 @ 256, 82.64 @ 320 (EvoNormS) |
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* `regnetz_d8_evos` - 83.42 @ 256, 84.04 @ 320 (EvoNormS) |
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* `xception41p` - 82 @ 299 (timm pre-act) |
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* `xception65` - 83.17 @ 299 |
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* `xception65p` - 83.14 @ 299 (timm pre-act) |
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* `resnext101_64x4d` - 82.46 @ 224, 83.16 @ 288 |
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* `seresnext101_32x8d` - 83.57 @ 224, 84.270 @ 288 |
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* `resnetrs200` - 83.85 @ 256, 84.44 @ 320 |
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* HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon) |
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* SwinTransformer-V2 implementation added. Submitted by [Christoph Reich](https://github.com/ChristophReich1996). Training experiments and model changes by myself are ongoing so expect compat breaks. |
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* Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2 |
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* MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets |
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* PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer |
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* VOLO models w/ weights adapted from https://github.com/sail-sg/volo |
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* Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc |
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* Enhance support for alternate norm + act ('NormAct') layers added to a number of models, esp EfficientNet/MobileNetV3, RegNet, and aligned Xception |
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* Grouped conv support added to EfficientNet family |
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* Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler |
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* Gradient checkpointing support added to many models |
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* `forward_head(x, pre_logits=False)` fn added to all models to allow separate calls of `forward_features` + `forward_head` |
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* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `foward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head` |
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* [Chris Hughes](https://github.com/Chris-hughes10) posted an exhaustive run through of `timm` on his blog yesterday. Well worth a read. [Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055) |
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* I'm currently prepping to merge the `norm_norm_norm` branch back to master (ver 0.6.x) in next week or so. |
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* The changes are more extensive than usual and may destabilize and break some model API use (aiming for full backwards compat). So, beware `pip install git+https://github.com/rwightman/pytorch-image-models` installs! |
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* `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. |
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* Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon.... |
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* Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features |
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* Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way... |
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* `mnasnet_small` - 65.6 top-1 |
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* `mobilenetv2_050` - 65.9 |
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* `lcnet_100/075/050` - 72.1 / 68.8 / 63.1 |
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* `semnasnet_075` - 73 |
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* `fbnetv3_b/d/g` - 79.1 / 79.7 / 82.0 |
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* TinyNet models added by [rsomani95](https://github.com/rsomani95) |
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* LCNet added via MobileNetV3 architecture |
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