PyTorch Image Models

• What's New
• Introduction
• Models
• Features
• Results
• Getting Started (Documentation)
• Train, Validation, Inference Scripts
• Awesome PyTorch Resources
• Licenses
• Citing

What's New

❗Updates after Oct 10, 2022 are available in version >= 0.9❗

• Many changes since the last 0.6.x stable releases. They were previewed in 0.8.x dev releases but not everyone transitioned.
• timm.models.layers moved to timm.layers:
  • from timm.models.layers import name will still work via deprecation mapping (but please transition to timm.layers).
  • import timm.models.layers.module or from timm.models.layers.module import name needs to be changed now.
• Builder, helper, non-model modules in timm.models have a _ prefix added, ie timm.models.helpers -> timm.models._helpers, there are temporary deprecation mapping files but those will be removed.
• All models now support architecture.pretrained_tag naming (ex resnet50.rsb_a1).
  • The pretrained_tag is the specific weight variant (different head) for the architecture.
  • Using only architecture defaults to the first weights in the default_cfgs for that model architecture.
  • In adding pretrained tags, many model names that existed to differentiate were renamed to use the tag (ex: vit_base_patch16_224_in21k -> vit_base_patch16_224.augreg_in21k). There are deprecation mappings for these.
• A number of models had their checkpoints remaped to match architecture changes needed to better support features_only=True, there are checkpoint_filter_fn methods in any model module that was remapped. These can be passed to timm.models.load_checkpoint(..., filter_fn=timm.models.swin_transformer_v2.checkpoint_filter_fn) to remap your existing checkpoint.
• The Hugging Face Hub (https://huggingface.co/timm) is now the primary source for timm weights. Model cards include link to papers, original source, license.
• Previous 0.6.x can be cloned from 0.6.x branch or installed via pip with version.

Aug 8, 2024

• Add RDNet ('DenseNets Reloaded', https://arxiv.org/abs/2403.19588), thanks Donghyun Kim

July 28, 2024

• 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

July 26, 2024

• More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models

• Impressive MobileNet-V1 and EfficientNet-B0 baseline challenges (https://huggingface.co/blog/rwightman/mobilenet-baselines)

• 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

• Fix vit reg token init, thanks Promisery
• Other misc fixes

June 24, 2024

• 3 more MobileNetV4 hyrid weights with different MQA weight init scheme

• florence2 weight loading in DaViT model

June 12, 2024

• MobileNetV4 models and initial set of timm trained weights added: