Upload folder using huggingface_hub

Dockerfile

@@ -16,4 +16,4 @@ COPY --chown=user train.sh pytorch-image-models
 RUN chmod +x pytorch-image-models/train.sh
 
 COPY --chown=user . /app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["python", "app.py"]

README.md

@@ -1,5 +1,5 @@
 ---
-title: ImagenetTraining-imagenet-1k-random-20.0-frac-1over2
+title: ImageNetTraining20.0-frac-1over2
 emoji: 😻
 colorFrom: yellow
 colorTo: blue

app.py

@@ -1,5 +1,7 @@
+
 import os
-from fastapi import FastAPI
+import gradio as gr
+
 import wandb
 from huggingface_hub import HfApi
 
@@ -8,40 +10,43 @@ API = HfApi(token=TOKEN)
 wandb_api_key = os.environ.get('wandb_api_key')
 wandb.login(key=wandb_api_key)
 
-EXPERIMENT = "imagenet-1k-random-20.0-frac-1over2"
-# Input dataset
-INPUT = f"datacomp/{EXPERIMENT}"
-# Output for files and Space ID
-OUTPUT = f"datacomp/ImagenetTraining-{EXPERIMENT}"
+random_num = f"20.0"
+subset = f"frac-1over2"
+experiment_name = f"ImageNetTraining20.0-frac-1over2"
+experiment_repo = f"datacomp/ImageNetTraining20.0-frac-1over2"
 
-app = FastAPI()
-
-@app.get("/")
 def start_train():
-    os.system("echo 'Space started!'")
-    os.system("echo pwd")
+    os.system("echo '#### pwd'")
     os.system("pwd")
-    os.system("echo ls")
+    os.system("echo '#### ls'")
     os.system("ls")
-    os.system("echo 'creating dataset for output files if it doesn't exist...'")
+    # Create a place to put the output.
+    os.system("echo 'Creating results output repository in case it does not exist yet...'")
     try:
-        API.create_repo(repo_id=OUTPUT, repo_type="dataset",)
+        API.create_repo(repo_id=f"datacomp/ImageNetTraining20.0-frac-1over2", repo_type="dataset",)
+        os.system(f"echo 'Created results output repository datacomp/ImageNetTraining20.0-frac-1over2'")
     except:
+        os.system("echo 'Already there; skipping.'")
         pass
-    #space_variables = API.get_space_variables(repo_id=SPACE_ID)
-    #if 'STATUS' not in space_variables or space_variables['STATUS'] != 'COMPUTING':
     os.system("echo 'Beginning processing.'")
-    # API.add_space_variable(repo_id=SPACE_ID, key='STATUS', value='COMPUTING')
     # Handles CUDA OOM errors.
     os.system(f"export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
-    # Prints more informative CUDA errors (I think? I've forgotten now.)
-    os.system("export CUDA_LAUNCH_BLOCKING=1")
     os.system("echo 'Okay, trying training.'")
-    os.system(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/{INPUT} --log-wandb --experiment {EXPERIMENT} --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4")
-    os.system("echo ls")
+    os.system(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/datacomp/imagenet-1k-random-20.0-frac-1over2 --log-wandb --wandb-project ImageNetTraining20.0-frac-1over2 --experiment ImageNetTraining20.0-frac-1over2 --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4")
+    os.system("echo 'Done'.")
     os.system("ls")
+    # Upload output to repository
     os.system("echo 'trying to upload...'")
-    API.upload_large_folder(folder_path="/app", repo_id=OUTPUT, repo_type="dataset",)
-    #API.add_space_variable(repo_id=SPACE_ID, key='STATUS', value='NOT_COMPUTING')
-    #API.pause_space(SPACE_ID)
-    return {"Completed": "!"}
+    API.upload_folder(folder_path="/app", repo_id=f"datacomp/ImageNetTraining20.0-frac-1over2", repo_type="dataset",)
+    API.pause_space(experiment_repo)
+
+def run():
+    with gr.Blocks() as app:
+        gr.Markdown(f"Randomization: 20.0")
+        gr.Markdown(f"Subset: frac-1over2")
+        start = gr.Button("Start")
+        start.click(start_train)
+    app.launch(server_name="0.0.0.0", server_port=7860)
+    
+if __name__ == '__main__':
+    run()

pytorch-image-models/CONTRIBUTING.md

@@ -10,7 +10,7 @@ Code linting and auto-format (black) are not currently in place but open to cons
 
 A few specific differences from Google style (or black)
 1. Line length is 120 char. Going over is okay in some cases (e.g. I prefer not to break URL across lines).
-2. Hanging indents are always prefered, please avoid aligning arguments with closing brackets or braces.
+2. Hanging indents are always preferred, please avoid aligning arguments with closing brackets or braces.
 
 Example, from Google guide, but this is a NO here:
 ```

pytorch-image-models/README.md

@@ -12,6 +12,37 @@
 
 ## What's New
 
+## Jan 19, 2025
+* Fix loading of LeViT safetensor weights, remove conversion code which should have been deactivated
+* Add 'SO150M' ViT weights trained with SBB recipes, decent results, but not optimal shape for ImageNet-12k/1k pretrain/ft
+  * `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k` - 86.7% top-1
+  * `vit_so150m_patch16_reg4_gap_384.sbb_e250_in12k_ft_in1k` - 87.4% top-1
+  * `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k`
+* Misc typing, typo, etc. cleanup
+* 1.0.14 release to get above LeViT fix out
+
+## Jan 9, 2025
+* Add support to train and validate in pure `bfloat16` or `float16`
+* `wandb` project name arg added by https://github.com/caojiaolong, use arg.experiment for name
+* Fix old issue w/ checkpoint saving not working on filesystem w/o hard-link support (e.g. FUSE fs mounts)
+* 1.0.13 release
+
+## Jan 6, 2025
+* Add `torch.utils.checkpoint.checkpoint()` wrapper in `timm.models` that defaults `use_reentrant=False`, unless `TIMM_REENTRANT_CKPT=1` is set in env.
+
+## Dec 31, 2024
+* `convnext_nano` 384x384 ImageNet-12k pretrain & fine-tune. https://huggingface.co/models?search=convnext_nano%20r384
+* Add AIM-v2 encoders from https://github.com/apple/ml-aim, see on Hub: https://huggingface.co/models?search=timm%20aimv2
+* Add PaliGemma2 encoders from https://github.com/google-research/big_vision to existing PaliGemma, see on Hub: https://huggingface.co/models?search=timm%20pali2
+* Add missing L/14 DFN2B 39B CLIP ViT, `vit_large_patch14_clip_224.dfn2b_s39b`
+* Fix existing `RmsNorm` layer & fn to match standard formulation, use PT 2.5 impl when possible. Move old impl to `SimpleNorm` layer, it's LN w/o centering or bias. There were only two `timm` models using it, and they have been updated.
+* Allow override of `cache_dir` arg for model creation
+* Pass through `trust_remote_code` for HF datasets wrapper
+* `inception_next_atto` model added by creator
+* Adan optimizer caution, and Lamb decoupled weighgt decay options
+* Some feature_info metadata fixed by https://github.com/brianhou0208
+* All OpenCLIP and JAX (CLIP, SigLIP, Pali, etc) model weights that used load time remapping were given their own HF Hub instances so that they work with `hf-hub:` based loading, and thus will work with new Transformers `TimmWrapperModel`
+
 ## Nov 28, 2024
 * More optimizers
   * Add MARS optimizer (https://arxiv.org/abs/2411.10438, https://github.com/AGI-Arena/MARS)
@@ -94,7 +125,6 @@ Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weight
   * [mobilenetv3_large_150d.ra4_e3600_r256_in1k](http://hf.co/timm/mobilenetv3_large_150d.ra4_e3600_r256_in1k) - 81.81 @ 320, 80.94 @ 256
   * [mobilenetv3_large_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv3_large_100.ra4_e3600_r224_in1k) - 77.16 @ 256, 76.31 @ 224
 
-
 ### Aug 21, 2024
 * Updated SBB ViT models trained on ImageNet-12k and fine-tuned on ImageNet-1k, challenging quite a number of much larger, slower models
 
@@ -216,7 +246,7 @@ Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weight
 ### 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`)
+* Add `normalize=` flag for transforms, return non-normalized torch.Tensor with original dytpe (for `chug`)
 * Version 1.0.3 release
 
 ### May 11, 2024
@@ -248,7 +278,7 @@ Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weight
 ### 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.
+* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or directly.
 ```python
 model = timm.create_model('vit_base_patch16_224')
 final_feat, intermediates = model.forward_intermediates(input) 
@@ -297,53 +327,6 @@ torch.Size([2, 768, 32, 32])
 * 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
-
-### 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
-
-### 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
-
 ## Introduction
 
 Py**T**orch **Im**age **M**odels (`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.
@@ -486,7 +469,7 @@ Included optimizers available via `timm.optim.create_optimizer_v2` factory metho
 * `madgrad` an implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075
 * `mars` MARS optimizer from https://github.com/AGI-Arena/MARS - https://arxiv.org/abs/2411.10438
 * `nadam` an implementation of Adam w/ Nesterov momentum
-* `nadamw` an impementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
+* `nadamw` an implementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency
 * `novograd` by [Masashi Kimura](https://github.com/convergence-lab/novograd) - https://arxiv.org/abs/1905.11286
 * `radam` by [Liyuan Liu](https://github.com/LiyuanLucasLiu/RAdam) - https://arxiv.org/abs/1908.03265
 * `rmsprop_tf` adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour

pytorch-image-models/UPGRADING.md

@@ -1,10 +1,10 @@
 # Upgrading from previous versions
 
-I generally try to maintain code interface and especially model weight compability across many `timm` versions. Sometimes there are exceptions.
+I generally try to maintain code interface and especially model weight compatibility across many `timm` versions. Sometimes there are exceptions.
 
 ## Checkpoint remapping
 
-Pretrained weight remapping is handled by `checkpoint_filter_fn` in a model implementation module. This remaps old pretrained checkpoints to new, and also 3rd party (original) checkpoints to `timm` format if the model was modified when brough into `timm`.
+Pretrained weight remapping is handled by `checkpoint_filter_fn` in a model implementation module. This remaps old pretrained checkpoints to new, and also 3rd party (original) checkpoints to `timm` format if the model was modified when brought into `timm`.
 
 The `checkpoint_filter_fn` is automatically called when loading pretrained weights via `pretrained=True`, but they can be called manually if you call the fn directly with the current model instance and old state dict.
 
@@ -19,6 +19,6 @@ Many changes were made since the 0.6.x stable releases. They were previewed in 0
   * 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.
+* A number of models had their checkpoints remapped 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](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) branch or installed via pip with version.

pytorch-image-models/benchmark.py

@@ -88,8 +88,8 @@ parser.add_argument('-b', '--batch-size', default=256, type=int,
                     metavar='N', help='mini-batch size (default: 256)')
 parser.add_argument('--img-size', default=None, type=int,
                     metavar='N', help='Input image dimension, uses model default if empty')
-parser.add_argument('--input-size', default=None, nargs=3, type=int,
-                    metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
+parser.add_argument('--input-size', default=None, nargs=3, type=int, metavar='N',
+                    help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
 parser.add_argument('--use-train-size', action='store_true', default=False,
                     help='Run inference at train size, not test-input-size if it exists.')
 parser.add_argument('--num-classes', type=int, default=None,

pytorch-image-models/hfdocs/source/changes.mdx

@@ -1,5 +1,146 @@
 # Changelog
 
+## Jan 19, 2025
+* Fix loading of LeViT safetensor weights, remove conversion code which should have been deactivated
+* Add 'SO150M' ViT weights trained with SBB recipes, decent results, but not optimal shape for ImageNet-12k/1k pretrain/ft
+  * `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k` - 86.7% top-1
+  * `vit_so150m_patch16_reg4_gap_384.sbb_e250_in12k_ft_in1k` - 87.4% top-1
+  * `vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k`
+* Misc typing, typo, etc. cleanup
+* 1.0.14 release to get above LeViT fix out
+
+## Jan 9, 2025
+* Add support to train and validate in pure `bfloat16` or `float16`
+* `wandb` project name arg added by https://github.com/caojiaolong, use arg.experiment for name
+* Fix old issue w/ checkpoint saving not working on filesystem w/o hard-link support (e.g. FUSE fs mounts)
+* 1.0.13 release
+
+## Jan 6, 2025
+* Add `torch.utils.checkpoint.checkpoint()` wrapper in `timm.models` that defaults `use_reentrant=False`, unless `TIMM_REENTRANT_CKPT=1` is set in env.
+
+## Dec 31, 2024
+* `convnext_nano` 384x384 ImageNet-12k pretrain & fine-tune. https://huggingface.co/models?search=convnext_nano%20r384
+* Add AIM-v2 encoders from https://github.com/apple/ml-aim, see on Hub: https://huggingface.co/models?search=timm%20aimv2
+* Add PaliGemma2 encoders from https://github.com/google-research/big_vision to existing PaliGemma, see on Hub: https://huggingface.co/models?search=timm%20pali2
+* Add missing L/14 DFN2B 39B CLIP ViT, `vit_large_patch14_clip_224.dfn2b_s39b`
+* Fix existing `RmsNorm` layer & fn to match standard formulation, use PT 2.5 impl when possible. Move old impl to `SimpleNorm` layer, it's LN w/o centering or bias. There were only two `timm` models using it, and they have been updated.
+* Allow override of `cache_dir` arg for model creation
+* Pass through `trust_remote_code` for HF datasets wrapper
+* `inception_next_atto` model added by creator
+* Adan optimizer caution, and Lamb decoupled weighgt decay options
+* Some feature_info metadata fixed by https://github.com/brianhou0208
+* All OpenCLIP and JAX (CLIP, SigLIP, Pali, etc) model weights that used load time remapping were given their own HF Hub instances so that they work with `hf-hub:` based loading, and thus will work with new Transformers `TimmWrapperModel`
+
+## Nov 28, 2024
+* More optimizers
+  * Add MARS optimizer (https://arxiv.org/abs/2411.10438, https://github.com/AGI-Arena/MARS)
+  * Add LaProp optimizer (https://arxiv.org/abs/2002.04839, https://github.com/Z-T-WANG/LaProp-Optimizer)
+  * Add masking from 'Cautious Optimizers' (https://arxiv.org/abs/2411.16085, https://github.com/kyleliang919/C-Optim) to Adafactor, Adafactor Big Vision, AdamW (legacy), Adopt, Lamb, LaProp, Lion, NadamW, RMSPropTF, SGDW
+  * Cleanup some docstrings and type annotations re optimizers and factory
+* Add MobileNet-V4 Conv Medium models pretrained on in12k and fine-tuned in1k @ 384x384
+  * https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k_ft_in1k
+  * https://huggingface.co/timm/mobilenetv4_conv_medium.e250_r384_in12k
+  * https://huggingface.co/timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
+  * https://huggingface.co/timm/mobilenetv4_conv_medium.e180_r384_in12k
+* Add small cs3darknet, quite good for the speed
+  * https://huggingface.co/timm/cs3darknet_focus_s.ra4_e3600_r256_in1k
+
+## Nov 12, 2024
+* Optimizer factory refactor
+  * New factory works by registering optimizers using an OptimInfo dataclass w/ some key traits
+  * Add `list_optimizers`, `get_optimizer_class`, `get_optimizer_info` to reworked `create_optimizer_v2` fn to explore optimizers, get info or class
+  * deprecate `optim.optim_factory`, move fns to `optim/_optim_factory.py` and `optim/_param_groups.py` and encourage import via `timm.optim`
+* Add Adopt (https://github.com/iShohei220/adopt) optimizer
+* Add 'Big Vision' variant of Adafactor (https://github.com/google-research/big_vision/blob/main/big_vision/optax.py) optimizer
+* Fix original Adafactor to pick better factorization dims for convolutions
+* Tweak LAMB optimizer with some improvements in torch.where functionality since original, refactor clipping a bit
+* dynamic img size support in vit, deit, eva improved to support resize from non-square patch grids, thanks https://github.com/wojtke
+*
+## Oct 31, 2024
+Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weights. See https://huggingface.co/blog/rwightman/resnet-trick-or-treat
+
+## Oct 19, 2024
+* Cleanup torch amp usage to avoid cuda specific calls, merge support for Ascend (NPU) devices from [MengqingCao](https://github.com/MengqingCao) that should work now in PyTorch 2.5 w/ new device extension autoloading feature. Tested Intel Arc (XPU) in Pytorch 2.5 too and it (mostly) worked.
+
+## Oct 16, 2024
+* Fix error on importing from deprecated path `timm.models.registry`, increased priority of existing deprecation warnings to be visible
+* Port weights of InternViT-300M (https://huggingface.co/OpenGVLab/InternViT-300M-448px) to `timm` as `vit_intern300m_patch14_448`
+
+### Oct 14, 2024
+* Pre-activation (ResNetV2) version of 18/18d/34/34d ResNet model defs added by request (weights pending)
+* Release 1.0.10
+
+### Oct 11, 2024
+* MambaOut (https://github.com/yuweihao/MambaOut) model & weights added. A cheeky take on SSM vision models w/o the SSM (essentially ConvNeXt w/ gating). A mix of original weights + custom variations & weights.
+
+|model                                                                                                                |img_size|top1  |top5  |param_count|
+|---------------------------------------------------------------------------------------------------------------------|--------|------|------|-----------|
+|[mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_r384_in12k_ft_in1k)|384     |87.506|98.428|101.66     |
+|[mambaout_base_plus_rw.sw_e150_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|288     |86.912|98.236|101.66     |
+|[mambaout_base_plus_rw.sw_e150_in12k_ft_in1k](http://huggingface.co/timm/mambaout_base_plus_rw.sw_e150_in12k_ft_in1k)|224     |86.632|98.156|101.66     |
+|[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k)                  |288     |84.974|97.332|86.48      |
+|[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k)                  |288     |84.962|97.208|94.45      |
+|[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k)                |288     |84.832|97.27 |88.83      |
+|[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k)                                                  |288     |84.72 |96.93 |84.81      |
+|[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k)                          |288     |84.598|97.098|48.5       |
+|[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k)                                                |288     |84.5  |96.974|48.49      |
+|[mambaout_base_wide_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_wide_rw.sw_e500_in1k)                  |224     |84.454|96.864|94.45      |
+|[mambaout_base_tall_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_tall_rw.sw_e500_in1k)                  |224     |84.434|96.958|86.48      |
+|[mambaout_base_short_rw.sw_e500_in1k](http://huggingface.co/timm/mambaout_base_short_rw.sw_e500_in1k)                |224     |84.362|96.952|88.83      |
+|[mambaout_base.in1k](http://huggingface.co/timm/mambaout_base.in1k)                                                  |224     |84.168|96.68 |84.81      |
+|[mambaout_small.in1k](http://huggingface.co/timm/mambaout_small.in1k)                                                |224     |84.086|96.63 |48.49      |
+|[mambaout_small_rw.sw_e450_in1k](http://huggingface.co/timm/mambaout_small_rw.sw_e450_in1k)                          |224     |84.024|96.752|48.5       |
+|[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k)                                                  |288     |83.448|96.538|26.55      |
+|[mambaout_tiny.in1k](http://huggingface.co/timm/mambaout_tiny.in1k)                                                  |224     |82.736|96.1  |26.55      |
+|[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k)                                                  |288     |81.054|95.718|9.14       |
+|[mambaout_kobe.in1k](http://huggingface.co/timm/mambaout_kobe.in1k)                                                  |224     |79.986|94.986|9.14       |
+|[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k)                                                |288     |79.848|95.14 |7.3        |
+|[mambaout_femto.in1k](http://huggingface.co/timm/mambaout_femto.in1k)                                                |224     |78.87 |94.408|7.3        |
+
+* SigLIP SO400M ViT fine-tunes on ImageNet-1k @ 378x378, added 378x378 option for existing SigLIP 384x384 models
+  *  [vit_so400m_patch14_siglip_378.webli_ft_in1k](https://huggingface.co/timm/vit_so400m_patch14_siglip_378.webli_ft_in1k) - 89.42 top-1
+  *  [vit_so400m_patch14_siglip_gap_378.webli_ft_in1k](https://huggingface.co/timm/vit_so400m_patch14_siglip_gap_378.webli_ft_in1k) - 89.03
+* SigLIP SO400M ViT encoder from recent multi-lingual (i18n) variant, patch16 @ 256x256 (https://huggingface.co/timm/ViT-SO400M-16-SigLIP-i18n-256). OpenCLIP update pending.
+* Add two ConvNeXt 'Zepto' models & weights (one w/ overlapped stem and one w/ patch stem). Uses RMSNorm, smaller than previous 'Atto', 2.2M params.
+  * [convnext_zepto_rms_ols.ra4_e3600_r224_in1k](https://huggingface.co/timm/convnext_zepto_rms_ols.ra4_e3600_r224_in1k) - 73.20 top-1 @ 224
+  * [convnext_zepto_rms.ra4_e3600_r224_in1k](https://huggingface.co/timm/convnext_zepto_rms.ra4_e3600_r224_in1k) - 72.81 @ 224
+
+### Sept 2024
+* Add a suite of tiny test models for improved unit tests and niche low-resource applications (https://huggingface.co/blog/rwightman/timm-tiny-test)
+* Add MobileNetV4-Conv-Small (0.5x) model (https://huggingface.co/posts/rwightman/793053396198664)
+  * [mobilenetv4_conv_small_050.e3000_r224_in1k](http://hf.co/timm/mobilenetv4_conv_small_050.e3000_r224_in1k) - 65.81 top-1 @ 256, 64.76 @ 224
+* Add MobileNetV3-Large variants trained with MNV4 Small recipe
+  * [mobilenetv3_large_150d.ra4_e3600_r256_in1k](http://hf.co/timm/mobilenetv3_large_150d.ra4_e3600_r256_in1k) - 81.81 @ 320, 80.94 @ 256
+  * [mobilenetv3_large_100.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv3_large_100.ra4_e3600_r224_in1k) - 77.16 @ 256, 76.31 @ 224
+
+### Aug 21, 2024
+* Updated SBB ViT models trained on ImageNet-12k and fine-tuned on ImageNet-1k, challenging quite a number of much larger, slower models
+
+| model | top1 | top5 | param_count | img_size |
+| -------------------------------------------------- | ------ | ------ | ----------- | -------- |
+| [vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k](https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k) | 87.438 | 98.256 | 64.11 | 384 |
+| [vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k](https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k) | 86.608 | 97.934 | 64.11 | 256 |
+| [vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k) | 86.594 | 98.02 | 60.4 | 384 |
+| [vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k) | 85.734 | 97.61 | 60.4 | 256 |
+* MobileNet-V1 1.25, EfficientNet-B1, & ResNet50-D weights w/ MNV4 baseline challenge recipe
+
+| model                                                                                                                    | top1   | top5   | param_count | img_size |
+|--------------------------------------------------------------------------------------------------------------------------|--------|--------|-------------|----------|
+| [resnet50d.ra4_e3600_r224_in1k](http://hf.co/timm/resnet50d.ra4_e3600_r224_in1k)                                         | 81.838 | 95.922 | 25.58       | 288      |
+| [efficientnet_b1.ra4_e3600_r240_in1k](http://hf.co/timm/efficientnet_b1.ra4_e3600_r240_in1k)                             | 81.440 | 95.700 | 7.79        | 288      |
+| [resnet50d.ra4_e3600_r224_in1k](http://hf.co/timm/resnet50d.ra4_e3600_r224_in1k)                                         | 80.952 | 95.384 | 25.58       | 224      |
+| [efficientnet_b1.ra4_e3600_r240_in1k](http://hf.co/timm/efficientnet_b1.ra4_e3600_r240_in1k)                             | 80.406 | 95.152 | 7.79        | 240      |
+| [mobilenetv1_125.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_125.ra4_e3600_r224_in1k)                             | 77.600 | 93.804 | 6.27        | 256      |
+| [mobilenetv1_125.ra4_e3600_r224_in1k](http://hf.co/timm/mobilenetv1_125.ra4_e3600_r224_in1k)                             | 76.924 | 93.234 | 6.27        | 224      |
+
+* Add SAM2 (HieraDet) backbone arch & weight loading support
+* Add Hiera Small weights trained w/ abswin pos embed on in12k & fine-tuned on 1k
+
+|model                            |top1  |top5  |param_count|
+|---------------------------------|------|------|-----------|
+|hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k    |84.912|97.260|35.01      |
+|hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k |84.560|97.106|35.01      |
+
 ### Aug 8, 2024
 * Add RDNet ('DenseNets Reloaded', https://arxiv.org/abs/2403.19588), thanks [Donghyun Kim](https://github.com/dhkim0225)
 
@@ -93,7 +234,7 @@
 ### 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`)
+* Add `normalize=` flag for transforms, return non-normalized torch.Tensor with original dytpe (for `chug`)
 * Version 1.0.3 release
 
 ### May 11, 2024
@@ -125,7 +266,7 @@
 ### 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.
+* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or directly.
 ```python
 model = timm.create_model('vit_base_patch16_224')
 final_feat, intermediates = model.forward_intermediates(input)
@@ -360,7 +501,7 @@ Datasets & transform refactoring
 * 0.8.15dev0
 
 ### Feb 20, 2023
-* 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
+* Add 320x320 `convnext_large_mlp.clip_laion2b_ft_320` and `convnext_large_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
 
 ### Feb 16, 2023
@@ -745,7 +886,7 @@ More models, more fixes
 * 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`
+* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `forward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head`
 
 ### Feb 2, 2022
 * [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)
@@ -1058,7 +1199,7 @@ More models, more fixes
 * 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`
+* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `forward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head`
 
 ### Feb 2, 2022
 * [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)

pytorch-image-models/hfdocs/source/models.mdx

@@ -33,7 +33,7 @@ A more exciting view (with pretty pictures) of the models within `timm` can be f
 ## DLA
 
 * Implementation: [dla.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py)
-* Paper: https://arxiv.org/abs/1707.06484
+* Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
 * Code: https://github.com/ucbdrive/dla
 
 ## Dual-Path Networks
@@ -78,14 +78,14 @@ A more exciting view (with pretty pictures) of the models within `timm` can be f
 ## NASNet-A
 
 * Implementation: [nasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py)
-* Papers: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012
+* Paper: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012
 * Code: https://github.com/Cadene/pretrained-models.pytorch
 * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
 
 ## PNasNet-5
 
 * Implementation: [pnasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py)
-* Papers: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559
+* Paper: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559
 * Code: https://github.com/Cadene/pretrained-models.pytorch
 * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet
 

pytorch-image-models/hfdocs/source/models/adversarial-inception-v3.mdx

@@ -1,6 +1,6 @@
 # Adversarial Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 This particular model was trained for study of adversarial examples (adversarial training).
 
@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/advprop.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/big-transfer.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/csp-darknet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/csp-resnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/csp-resnext.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/densenet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/dla.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/dpn.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ecaresnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/efficientnet-pruned.mdx

@@ -1,6 +1,6 @@
 # EfficientNet (Knapsack Pruned)
 
-**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales  these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\),  width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a principled way.
+**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scale network width, depth, and resolution in a principled way.
 
 The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
 
@@ -79,7 +79,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/efficientnet.mdx

@@ -1,6 +1,6 @@
 # EfficientNet
 
-**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales  these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\),  width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a  principled way.
+**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scale network width, depth, and resolution in a principled way.
 
 The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
 
@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx

@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/fbnet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-inception-v3.mdx

@@ -1,6 +1,6 @@
 # (Gluon) Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
 
@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-resnet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-senet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/gloun-xception.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/hrnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/ig-resnext.mdx

@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/inception-v3.mdx

@@ -1,6 +1,6 @@
 # Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 ## How do I use this model on an image?
 
@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/inception-v4.mdx

@@ -72,7 +72,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/legacy-senet.mdx

@@ -75,7 +75,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mixnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mnasnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/nasnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/noisy-student.mdx

@@ -82,7 +82,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/pnasnet.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/regnetx.mdx

@@ -1,10 +1,10 @@
 # RegNetX
 
-**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
+**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w_{0} > 0 \\), and slope \\( w_{a} > 0 \\), and generates a different block width \\( u_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
 
-\\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\)
+\\( u_{j} = w_{0} + w_{a}\cdot{j} \\)
 
-For **RegNetX** we have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier).
+For **RegNetX** we have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w_{m} \geq 2 \\) (the width multiplier).
 
 ## How do I use this model on an image?
 
@@ -77,7 +77,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/regnety.mdx

@@ -1,10 +1,10 @@
 # RegNetY
 
-**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
+**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w_{0} > 0 \\), and slope \\( w_{a} > 0 \\), and generates a different block width \\( u_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
 
-\\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\)
+\\( u_{j} = w_{0} + w_{a}\cdot{j} \\)
 
-For **RegNetX** authors have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier).
+For **RegNetX** authors have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w_{m} \geq 2 \\) (the width multiplier).
 
 For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
 
@@ -79,7 +79,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/res2net.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/res2next.mdx

@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation
 

pytorch-image-models/hfdocs/source/models/resnest.mdx

@@ -1,6 +1,6 @@
 # ResNeSt
 
-A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: \\( V = \text{Concat} \\){\\( V^{1},V^{2},\cdots{V}^{K} \\)}. As in standard residual blocks, the final output \\( Y \\) of otheur Split-Attention block is produced using a shortcut connection: \\( Y=V+X \\), if the input and output feature-map share the same shape.  For blocks with a stride, an appropriate transformation \\( \mathcal{T} \\) is applied to the shortcut connection to align the output shapes:  \\( Y=V+\mathcal{T}(X) \\). For example, \\( \mathcal{T} \\) can be strided convolution or combined convolution-with-pooling.
+A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: \\( V = \text{Concat} \{ V^{1},V^{2},\cdots,{V}^{K} \} \\). As in standard residual blocks, the final output \\( Y \\) of otheur Split-Attention block is produced using a shortcut connection: \\( Y=V+X \\), if the input and output feature-map share the same shape.  For blocks with a stride, an appropriate transformation \\( \mathcal{T} \\) is applied to the shortcut connection to align the output shapes:  \\( Y=V+\mathcal{T}(X) \\). For example, \\( \mathcal{T} \\) can be strided convolution or combined convolution-with-pooling.
 
 ## How do I use this model on an image?
 
@@ -73,7 +73,7 @@ script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py)
 
 ## How do I train this model?
 
-You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
+You can follow the [timm recipe scripts](../training_script) for training a new model afresh.
 
 ## Citation