PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

Related tags

Deep Learning pytorch resnet pretrained-models mixnet pretrained-weights imagenet-classifier distributed-training dual-path-networks cnn-classification mobilenet-v2 mnasnet mobile-deep-learning mobilenetv3 efficientnet augmix randaugment efficientnet-training nfnets normalization-free-training vision-transformer-models
Overview

PyTorch Image Models

Sponsors

A big thank you to my GitHub Sponsors for their support!

In addition to the sponsors at the link above, I've received hardware and/or cloud resources from

I'm fortunate to be able to dedicate significant time and money of my own supporting this and other open source projects. However, as the projects increase in scope, outside support is needed to continue with the current trajectory of cloud services, hardware, and electricity costs.

What's New

Oct 19, 2021

Aug 18, 2021

  • Optimizer bonanza!
    • Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ timm bits branch)
    • Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA)
    • Some cleanup on all optimizers and factory. No more .data, a bit more consistency, unit tests for all!
    • SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself).
  • EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights.
  • Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested.

July 12, 2021

July 5-9, 2021

  • Add efficientnetv2_rw_t weights, a custom 'tiny' 13.6M param variant that is a bit better than (non NoisyStudent) B3 models. Both faster and better accuracy (at same or lower res)
    • top-1 82.34 @ 288x288 and 82.54 @ 320x320
  • Add SAM pretrained in1k weight for ViT B/16 (vit_base_patch16_sam_224) and B/32 (vit_base_patch32_sam_224) models.
  • Add 'Aggregating Nested Transformer' (NesT) w/ weights converted from official Flax impl. Contributed by Alexander Soare.
    • jx_nest_base - 83.534, jx_nest_small - 83.120, jx_nest_tiny - 81.426

June 23, 2021

  • Reproduce gMLP model training, gmlp_s16_224 trained to 79.6 top-1, matching paper. Hparams for this and other recent MLP training here

June 20, 2021

  • Release Vision Transformer 'AugReg' weights from How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
    • .npz weight loading support added, can load any of the 50K+ weights from the AugReg series
    • See example notebook from official impl for navigating the augreg weights
    • Replaced all default weights w/ best AugReg variant (if possible). All AugReg 21k classifiers work.
      • Highlights: vit_large_patch16_384 (87.1 top-1), vit_large_r50_s32_384 (86.2 top-1), vit_base_patch16_384 (86.0 top-1)
    • vit_deit_* renamed to just deit_*
    • Remove my old small model, replace with DeiT compatible small w/ AugReg weights
  • Add 1st training of my gmixer_24_224 MLP /w GLU, 78.1 top-1 w/ 25M params.
  • Add weights from official ResMLP release (https://github.com/facebookresearch/deit)
  • Add eca_nfnet_l2 weights from my 'lightweight' series. 84.7 top-1 at 384x384.
  • Add distilled BiT 50x1 student and 152x2 Teacher weights from Knowledge distillation: A good teacher is patient and consistent
  • NFNets and ResNetV2-BiT models work w/ Pytorch XLA now
    • weight standardization uses F.batch_norm instead of std_mean (std_mean wasn't lowered)
    • eps values adjusted, will be slight differences but should be quite close
  • Improve test coverage and classifier interface of non-conv (vision transformer and mlp) models
  • Cleanup a few classifier / flatten details for models w/ conv classifiers or early global pool
  • Please report any regressions, this PR touched quite a few models.

June 8, 2021

  • Add first ResMLP weights, trained in PyTorch XLA on TPU-VM w/ my XLA branch. 24 block variant, 79.2 top-1.
  • Add ResNet51-Q model w/ pretrained weights at 82.36 top-1.
    • NFNet inspired block layout with quad layer stem and no maxpool
    • Same param count (35.7M) and throughput as ResNetRS-50 but +1.5 top-1 @ 224x224 and +2.5 top-1 at 288x288

May 25, 2021

  • Add LeViT, Visformer, ConViT (PR by Aman Arora), Twins (PR by paper authors) transformer models
  • Add ResMLP and gMLP MLP vision models to the existing MLP Mixer impl
  • Fix a number of torchscript issues with various vision transformer models
  • Cleanup input_size/img_size override handling and improve testing / test coverage for all vision transformer and MLP models
  • More flexible pos embedding resize (non-square) for ViT and TnT. Thanks Alexander Soare
  • Add efficientnetv2_rw_m model and weights (started training before official code). 84.8 top-1, 53M params.

May 14, 2021

  • Add EfficientNet-V2 official model defs w/ ported weights from official Tensorflow/Keras impl.
    • 1k trained variants: tf_efficientnetv2_s/m/l
    • 21k trained variants: tf_efficientnetv2_s/m/l_in21k
    • 21k pretrained -> 1k fine-tuned: tf_efficientnetv2_s/m/l_in21ft1k
    • v2 models w/ v1 scaling: tf_efficientnetv2_b0 through b3
    • Rename my prev V2 guess efficientnet_v2s -> efficientnetv2_rw_s
    • Some blank efficientnetv2_* models in-place for future native PyTorch training

May 5, 2021

  • Add MLP-Mixer models and port pretrained weights from Google JAX impl
  • Add CaiT models and pretrained weights from FB
  • Add ResNet-RS models and weights from TF. Thanks Aman Arora
  • Add CoaT models and weights. Thanks Mohammed Rizin
  • Add new ImageNet-21k weights & finetuned weights for TResNet, MobileNet-V3, ViT models. Thanks mrT
  • Add GhostNet models and weights. Thanks Kai Han
  • Update ByoaNet attention modules
    • Improve SA module inits
    • Hack together experimental stand-alone Swin based attn module and swinnet
    • Consistent '26t' model defs for experiments.
  • Add improved Efficientnet-V2S (prelim model def) weights. 83.8 top-1.
  • WandB logging support

April 13, 2021

April 12, 2021

  • Add ECA-NFNet-L1 (slimmed down F1 w/ SiLU, 41M params) trained with this code. 84% top-1 @ 320x320. Trained at 256x256.
  • Add EfficientNet-V2S model (unverified model definition) weights. 83.3 top-1 @ 288x288. Only trained single res 224. Working on progressive training.
  • Add ByoaNet model definition (Bring-your-own-attention) w/ SelfAttention block and corresponding SA/SA-like modules and model defs
  • Adabelief optimizer contributed by Juntang Zhuang

April 1, 2021

  • Add snazzy benchmark.py script for bulk timm model benchmarking of train and/or inference
  • Add Pooling-based Vision Transformer (PiT) models (from https://github.com/naver-ai/pit)
    • Merged distilled variant into main for torchscript compatibility
    • Some timm cleanup/style tweaks and weights have hub download support
  • Cleanup Vision Transformer (ViT) models
    • Merge distilled (DeiT) model into main so that torchscript can work
    • Support updated weight init (defaults to old still) that closer matches original JAX impl (possibly better training from scratch)
    • Separate hybrid model defs into different file and add several new model defs to fiddle with, support patch_size != 1 for hybrids
    • Fix fine-tuning num_class changes (PiT and ViT) and pos_embed resizing (Vit) with distilled variants
    • nn.Sequential for block stack (does not break downstream compat)
  • TnT (Transformer-in-Transformer) models contributed by author (from https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT)
  • Add RegNetY-160 weights from DeiT teacher model
  • Add new NFNet-L0 w/ SE attn (rename nfnet_l0b->nfnet_l0) weights 82.75 top-1 @ 288x288
  • Some fixes/improvements for TFDS dataset wrapper

March 17, 2021

  • Add new ECA-NFNet-L0 (rename nfnet_l0c->eca_nfnet_l0) weights trained by myself.
    • 82.6 top-1 @ 288x288, 82.8 @ 320x320, trained at 224x224
    • Uses SiLU activation, approx 2x faster than dm_nfnet_f0 and 50% faster than nfnet_f0s w/ 1/3 param count
  • Integrate Hugging Face model hub into timm create_model and default_cfg handling for pretrained weight and config sharing (more on this soon!)
  • Merge HardCoRe NAS models contributed by https://github.com/yoniaflalo
  • Merge PyTorch trained EfficientNet-EL and pruned ES/EL variants contributed by DeGirum

March 7, 2021

  • First 0.4.x PyPi release w/ NFNets (& related), ByoB (GPU-Efficient, RepVGG, etc).
  • Change feature extraction for pre-activation nets (NFNets, ResNetV2) to return features before activation.
  • Tested with PyTorch 1.8 release. Updated CI to use 1.8.
  • Benchmarked several arch on RTX 3090, Titan RTX, and V100 across 1.7.1, 1.8, NGC 20.12, and 21.02. Some interesting performance variations to take note of https://gist.github.com/rwightman/bb59f9e245162cee0e38bd66bd8cd77f

Feb 18, 2021

  • Add pretrained weights and model variants for NFNet-F* models from DeepMind Haiku impl.
    • Models are prefixed with dm_. They require SAME padding conv, skipinit enabled, and activation gains applied in act fn.
    • These models are big, expect to run out of GPU memory. With the GELU activiation + other options, they are roughly 1/2 the inference speed of my SiLU PyTorch optimized s variants.
    • Original model results are based on pre-processing that is not the same as all other models so you'll see different results in the results csv (once updated).
    • Matching the original pre-processing as closely as possible I get these results:
      • dm_nfnet_f6 - 86.352
      • dm_nfnet_f5 - 86.100
      • dm_nfnet_f4 - 85.834
      • dm_nfnet_f3 - 85.676
      • dm_nfnet_f2 - 85.178
      • dm_nfnet_f1 - 84.696
      • dm_nfnet_f0 - 83.464

Feb 16, 2021

  • Add Adaptive Gradient Clipping (AGC) as per https://arxiv.org/abs/2102.06171. Integrated w/ PyTorch gradient clipping via mode arg that defaults to prev 'norm' mode. For backward arg compat, clip-grad arg must be specified to enable when using train.py.
    • AGC w/ default clipping factor --clip-grad .01 --clip-mode agc
    • PyTorch global norm of 1.0 (old behaviour, always norm), --clip-grad 1.0
    • PyTorch value clipping of 10, --clip-grad 10. --clip-mode value
    • AGC performance is definitely sensitive to the clipping factor. More experimentation needed to determine good values for smaller batch sizes and optimizers besides those in paper. So far I've found .001-.005 is necessary for stable RMSProp training w/ NFNet/NF-ResNet.

Feb 12, 2021

Feb 10, 2021

  • First Normalization-Free model training experiments done,
    • nf_resnet50 - 80.68 top-1 @ 288x288, 80.31 @ 256x256
    • nf_regnet_b1 - 79.30 @ 288x288, 78.75 @ 256x256
  • More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks')
  • Refinements to normalizer layer arg handling and normalizer+act layer handling in some models
  • Default AMP mode changed to native PyTorch AMP instead of APEX. Issues not being fixed with APEX. Native works with --channels-last and --torchscript model training, APEX does not.
  • Fix a few bugs introduced since last pypi release

Feb 8, 2021

  • Add several ResNet weights with ECA attention. 26t & 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352.
    • ecaresnet26t - 79.88 top-1 @ 320x320, 79.08 @ 256x256
    • ecaresnet50t - 82.35 top-1 @ 320x320, 81.52 @ 256x256
    • ecaresnet269d - 84.93 top-1 @ 352x352, 84.87 @ 320x320
  • Remove separate tiered (t) vs tiered_narrow (tn) ResNet model defs, all tn changed to t and t models removed (seresnext26t_32x4d only model w/ weights that was removed).
  • Support model default_cfgs with separate train vs test resolution test_input_size and remove extra _320 suffix ResNet model defs that were just for test.

Jan 30, 2021

  • Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based on paper

Jan 25, 2021

  • Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights from https://github.com/google-research/big_transfer
  • Add official R50+ViT-B/16 hybrid models + weights from https://github.com/google-research/vision_transformer
  • ImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) support
    • NOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learning
  • Add model defs and weights for DeiT Vision Transformer models from https://github.com/facebookresearch/deit
  • Refactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classes
  • Add Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train script
    • Ex: train.py /data/tfds --dataset tfds/oxford_iiit_pet --val-split test --model resnet50 -b 256 --amp --num-classes 37 --opt adamw --lr 3e-4 --weight-decay .001 --pretrained -j 2
  • Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tar
    • Run validation on full ImageNet-21k directly from tar w/ BiT model: validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --amp
  • Models in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handling

Jan 3, 2021

  • Add SE-ResNet-152D weights
    • 256x256 val, 0.94 crop top-1 - 83.75
    • 320x320 val, 1.0 crop - 84.36
  • Update results files

Introduction

PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.

The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything.

Models

All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated. Here are some example training hparams to get you started.

A full version of the list below with source links can be found in the documentation.

Features

Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP:

Results

Model validation results can be found in the documentation and in the results tables

Getting Started (Documentation)

My current documentation for timm covers the basics.

timmdocs is quickly becoming a much more comprehensive set of documentation for timm. A big thanks to Aman Arora for his efforts creating timmdocs.

paperswithcode is a good resource for browsing the models within timm.

Train, Validation, Inference Scripts

The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See documentation for some basics and training hparams for some train examples that produce SOTA ImageNet results.

Awesome PyTorch Resources

One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below.

Object Detection, Instance and Semantic Segmentation

Computer Vision / Image Augmentation

Knowledge Distillation

Metric Learning

Training / Frameworks

Licenses

Code

The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue.

Pretrained Weights

So far all of the pretrained weights available here are pretrained on ImageNet with a select few that have some additional pretraining (see extra note below). ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product.

Pretrained on more than ImageNet

Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions.

Citing

BibTeX

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}

Latest DOI

DOI

Comments
  • Efficientnetb1-b7 hyper parameters

    Efficientnetb1-b7 hyper parameters

    First of all thanks for the fantastic code!

    I am wondering if anyone has successfully reproduce (or close to it) the results for Efficientnetb1-b7? I am able to reproduce b0 with jiefengpeng's setting: ./distributed_train.sh 8 ../ImageNet/ --model efficientnet_b0 -b 256 --sched step --epochs 500 --decay-epochs 3 --decay-rate 0.963 --opt rmsproptf --opt-eps .001 -j 8 --warmup-epochs 5 --weight-decay 1e-5 --drop 0.2 --color-jitter .06 --model-ema --lr .128

    The same setting (with adjusted drop rate) for b1 came with only 78.11 (with EMA enabled), compared to 78.8% reported in the paper.

    opened by pichuang1984 40
  • default training hyper-parameters

    default training hyper-parameters

    Hi, Impressive work! The train scipts contains a large combination of various hyper-parameter options. However, there are different types of models, and even many models are contained even within the efficientnet part. I wonder whether you trained models with default ones. If not, do you plan to release model specific hyper-parameters? Thanks!

    opened by cxxgtxy 26
  • FX feature extraction

    FX feature extraction

    • Added timm.models.fx_features.FeatureGraphNet as another option for feature extraction. (works as a standalone commit)
    • Made all models traceable (2nd commit)
    • Tests to enforce all models traceable (3rd commit)

    ~Caveat - Right now we can only safely say it works in eval mode. Control flow that depends on the value of model.training is frozen into place by the tracing operation. So if the model was traced in eval mode, it stays that way (actually only those parts that were traced through, leaf modules and leaf functions respect the training mode). Therefore, we cannot expect model.train() to have the desired effect. This is a TODO, so right now there is a warning when the user tries to do model.train().~

    • This is sorted but hasn't been tested in anger.

    All local tests passed.

    EDIT - This feature has been added to torchvision https://github.com/pytorch/vision/commit/72d650ae0bf21f4d98cb8af5e308bddd88131d5e

    opened by alexander-soare 19
  • CUDA out of memory when load model

    CUDA out of memory when load model

    I have train mobilenetv3_large_100 using 8 2080Ti GPU, and the batch size is 128, which means 128 * 8 =1024 pictures every batch. When I resumed the model, there was an "CUDA out of memory" error. However, when I trained it again from scratch, there wasn't any error. I noticed that your codes of "helper.py" has loaded the model in cpu, it should be the solution for this bug, but why this happend? checkpoint = torch.load(checkpoint_path, map_location='cpu')

    Another interesting problem is that I find the acc@1 is very low in the first few epochs(nearly random property), and the eval_loss even rises, why？？？ image

    opened by Andy1621 19
  • ViT Training Details

    ViT Training Details

    Hi,

    In your code comments you are able to train a small version of the model to 75% top-1 accuracy. Could you give more details about the hyper-params used (like batch size, learning rate etc.)

    Thanks.

    opened by gupta-abhay 17
  • [FEATURE] Method to convert feature embeddings into predictions

    [FEATURE] Method to convert feature embeddings into predictions

    Is your feature request related to a problem? Please describe. Currently, saving both the feature embedding vector and the prediction vector requires two forward passes:

    predictions = model(inputs)
embeddings = model.forward_features(inputs)  # inefficient

    This is technically not necessary, since the embeddings are computed when you compute predictions.

    In addition, once you have your embeddings, there is no standardized method to convert the embeddings into predictions. For example, with ResNet Models I could do something like:

    predictions = model.fc(embeddings)

    ...but that does not generalize to other models since not every model has a fc layer.

    Describe the solution you'd like

    For every model to have a forward method (I suggest the name forward_predictions) which takes an embedding as input and outputs a prediction.

    For example, ResNet would go from:

    def forward(self, x):
    x = self.forward_features(x)
    x = self.global_pool(x)
    if self.drop_rate:
        x = F.dropout(x, p=float(self.drop_rate), training=self.training)
    x = self.fc(x)
    return x

    to:

    def forward_predictions(self, x):  # x is embedding vector
    x = self.global_pool(x)
    if self.drop_rate:
        x = F.dropout(x, p=float(self.drop_rate), training=self.training)
    x = self.fc(x)
    return x

def forward(self, x):
    x = self.forward_features(x)
    x = self.forward_predictions(x)
    return x

    Our inference code, then, would become:

    embeddings = model.forward_features(input)
predictions = model.forward_predictions(embeddings)  # no redundant compute
return embeddings, predictions

    ... and enables us to convert feature embedding vectors from a feature store into a prediction:

    embedding = feature_store.query(<interesting image>)
nearest_neighbor_embedding = feature_store.get_nearest_vector(embedding)
prediction = model.forward_predictions(nearest_neighbor_embedding)

    Describe alternatives you've considered Option 1: Two separate forward passes (for embeddings and predictions) for each image. Create "forward_predictions" helper functions for each different architecture we use to convert embeddings into predictions.

    Option 2: register a custom forward hook for every architecture which intercepts the embedding vector during the forward pass.

    enhancement 
    opened by crypdick 16
  • Cutmix

    Cutmix

    clovaai/CutMix-PyTorch: Official Pytorch implementation of CutMix regularizer GitHub: https://github.com/clovaai/CutMix-PyTorch

    Hi, I saw that you’ve been implementing mixup as an additional feature. However, if the model trained with mixup used as the backbone of object detector, it seems the performance of the detector degenerates.

    Could you please consider cutmix in addition to mixup?

    thanks!

    enhancement 
    opened by dandelin 16
  • FEATURE: mobilenetv2 0.35 training on ImageNet - train (and possibly add) smaller mobilenet v2/v3/mnasnet models

    FEATURE: mobilenetv2 0.35 training on ImageNet - train (and possibly add) smaller mobilenet v2/v3/mnasnet models

    Discussed in https://github.com/rwightman/pytorch-image-models/discussions/1020

    Originally posted by IgorKasianenko December 5, 2021 Hello, I want to train mobilenetv2 0.35 on Imagenet. I try to do it using example of https://rwightman.github.io/pytorch-image-models/training_hparam_examples/#mobilenetv3-large-100-75766-top-1-92542-top-5 After reading documentation of timm I assume that 0.35 depth model would be named like mobilenetv2_035 similarly to mobilenetv2_100 but I get error RuntimeError: Unknown model (mobilenetv2_035) Please advice how to add this model to timm so I can utilize ImageNet training script. Thanks

    enhancement help wanted 
    opened by IgorKasianenko 13
  • use `Image.Resampling` namespace for PIL mapping

    use `Image.Resampling` namespace for PIL mapping

    PIL version 9.1.0 shows a deprecation warning when accessing resampling constants via the Image namespace. The suggested namespace is Image.Resampling. This commit updates _pil_interpolation_to_str to use the Image.Resampling namespace.

    /tmp/ipykernel_11959/698124036.py:2: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
  Image.NEAREST: 'nearest',
/tmp/ipykernel_11959/698124036.py:3: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
  Image.BILINEAR: 'bilinear',
/tmp/ipykernel_11959/698124036.py:4: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
  Image.BICUBIC: 'bicubic',
/tmp/ipykernel_11959/698124036.py:5: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
  Image.BOX: 'box',
/tmp/ipykernel_11959/698124036.py:6: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
  Image.HAMMING: 'hamming',
/tmp/ipykernel_11959/698124036.py:7: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
  Image.LANCZOS: 'lanczos',
    opened by kaczmarj 12
  • Add option for ML-Decoder - an improved classification head

    Add option for ML-Decoder - an improved classification head

    While almost every aspect of ImageNet training had improved in the last couple of years (backbones, augmentations, loss,...), a plain classification head, GAP + fully connected, remains the default option. In our paper,"ML-Decoder: Scalable and Versatile Classification Head" ( https://github.com/Alibaba-MIIL/ML_Decoder ), we propose a new attention-based classification head, that not only improves results, but also provides better speed-accuracy tradeoff on various classification tasks - multi-label, single-label and zero shot.

    image

    image

    A technical note about the merge request - since each model has a unique coding style, systematically using a different classification head is challenging. This merge request enables ML-Decoder head to all CNNs (I specifically checked ResNet, ResNetD, EfficientNet, RgeNet and TResNet). For Transformers, the GAP operation is embedded inside the 'forward_features' pass, so it is hard to use a different classification head without editing each model separately.

    opened by mrT23 11
  • Model request: CSPNet

    Model request: CSPNet

    https://openaccess.thecvf.com/content_CVPRW_2020/papers/w28/Wang_CSPNet_A_New_Backbone_That_Can_Enhance_Learning_Capability_of_CVPRW_2020_paper.pdf

    The authors, as usual, claim that their models are faster, lighter, more accurate.

    It would be nice to add them to the repo.

    opened by ternaus 11
  • tf_efficientnet_b0_ap model was removed but is still in the doc

    tf_efficientnet_b0_ap model was removed but is still in the doc

    Describe the bug A clear and concise description of what the bug is.

    tf_efficientnet_b0_ap model was removed in https://github.com/rwightman/pytorch-image-models/commit/6a01101905e78007e5396f5ffdaae0c4725ba72c#diff-27c2bbd967991cbb5264f93cb5da34895fdab02424b2cc8c63d3d0768e65d47aL1833, but is still in doc https://github.com/rwightman/pytorch-image-models/blob/6a01101905e78007e5396f5ffdaae0c4725ba72c/docs/models/advprop.md#how-do-i-use-this-model-on-an-image

    To Reproduce Steps to reproduce the behavior:

    $ python -c "import timm; timm.create_model('tf_efficientnet_b0_ap', pretrained=True)"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/xwang/Developer/pytorch-image-models/timm/models/_factory.py", line 89, in create_model
    raise RuntimeError('Unknown model (%s)' % model_name)
RuntimeError: Unknown model (tf_efficientnet_b0_ap)
    bug 
    opened by xwang233 2
  • What batch size number other than 1024 have you tried when training a DeiT or ViT model?

    What batch size number other than 1024 have you tried when training a DeiT or ViT model?

    What batch size number other than batch size of 1024 have you tried when training a DeiT or ViT model? In the paper, DeiT (https://arxiv.org/abs/2012.12877), they used a batch size of 1024 and they mentioned that the learning rate should be scaled according to the batch size.

    However, I was wondering if you guys have any experience or successfully train a DeiT model with a batch size that is even less than 512? If yes, what accuracy did you achieve?

    This would be helpful for someone training on constrained resources that cannot train on a batch size of 1024.

    opened by CharlesLeeeee 0
  • [FEATURE] Script to convert weight from Jax to PyTorch

    [FEATURE] Script to convert weight from Jax to PyTorch

    Is your feature request related to a problem? Please describe. I am trying to create multiple checkpoints of ViT at different iterations. Are there any systematic way to perform such conversion?

    Describe the solution you'd like I would like to be able to convert JAX ViT model to a PyTorch model, similar to this model (https://huggingface.co/google/vit-base-patch16-224)

    Describe alternatives you've considered I have tried to start pre-training HF models on A100 but so far was not successful to reach to same accuracy.

    enhancement 
    opened by yazdanbakhsh 6
  • [FEATURE] BEIT pre-training model

    [FEATURE] BEIT pre-training model

    Is your feature request related to a problem? Please describe. There is no problem or bug

    Describe the solution you'd like I would like the implementation of BEIT pre-training pipeline in order to be able to manually pre-training the architecture

    Describe alternatives you've considered No

    Additional context No

    enhancement 
    opened by lorenzbaraldi 2
  • [BUG] ViT ImageNet1K weights

    [BUG] ViT ImageNet1K weights

    Describe the bug In Version: 0.5.4 for example, dose vit_tiny_patch16_224 means vit_tiny trained from scratch on ImageNet1K? However, in the current Version 0.6, vit_tiny_patch16_224 means vit_tiny pretrained on 21k and then fine-tuned on in1k, which is very misleading and leading to errors for down-stream experiments.

    To Reproduce Steps to reproduce the behavior: https://github.com/rwightman/pytorch-image-models/blob/18ec173f95aa220af753358bf860b16b6691edb2/timm/models/vision_transformer.py#L642

    Expected behavior Regular ImageNet-1K training without extra data knowledge.

    bug 
    opened by hellojialee 2
  • Pruned efficientnets don't respect the `in_chans` parameter

    Pruned efficientnets don't respect the `in_chans` parameter

    When creating a model using timm.create_model(arch, pretrained=True, in_chans=1, num_classes=1), single-channel input images can be used with tf_efficientnet_b2_ns, but not efficientnet_b3_pruned. The pruned models result in the following error:

      File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/efficientnet.py", line 557, in forward
    x = self.forward_features(x)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/efficientnet.py", line 540, in forward_features
    x = self.conv_stem(x)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1482, in _call_impl
    return forward_call(*args, **kwargs)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/layers/conv2d_same.py", line 30, in forward
    return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/layers/conv2d_same.py", line 17, in conv2d_same
    return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)

RuntimeError: Given groups=1, weight of size [40, 3, 3, 3], expected input[14, 1, 2459, 2459] to have 3 channels, but got 1 channels instead
    bug 
    opened by jphdotam 0
Releases(v0.8.2dev0)
Owner
Ross Wightman
Always learning, constantly curious. Building ML/AI systems, watching loss curves.
Ross Wightman
GitHub https://rwightman.github.io/pytorch-image-models/
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