vit tiny patch16 224.augreg in21k ft in1k

Providertimm
Categoryimage-classification
Licenseapache-2.0
Downloads2.0K
Stars3

Overview

The ViT-Tiny (patch16_224) is a lightweight Vision Transformer optimized for efficiency without sacrificing the architectural benefits of attention-based image processing. This specific variant leverages a robust pre-training strategy: it was trained on the extensive ImageNet-21k dataset with augmented regularization and subsequently fine-tuned on ImageNet-1k. For developers, this means the model provides a strong feature extraction baseline and faster convergence compared to models trained from scratch. It is ideal for edge deployment, real-time classification tasks, or as a backbone for downstream computer vision pipelines where latency and memory footprint are critical constraints. Integration is straightforward via the timm library, making it a plug-and-play replacement for traditional CNNs in small-scale production environments.

Highlights

  • Pre-trained on ImageNet-21k for superior feature generalization
  • Lightweight architecture ideal for edge device deployment
  • Seamless integration via the timm library
  • Optimized for low-latency image classification tasks
  • Apache-2.0 license ensures flexible commercial usage

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k")
tokenizer = AutoTokenizer.from_pretrained("timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k')
tokenizer = AutoTokenizer.from_pretrained('timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k')

Full Documentation

来源: HuggingFace

---
tags:

  • image-classification

  • timm

  • transformers

library_name: timm
license: apache-2.0
datasets:
  • imagenet-1k

  • imagenet-21k

---

Model card for vit_tiny_patch16_224.augreg_in21k_ft_in1k

A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman.

Model Details

  • Model Type: Image classification / feature backbone
  • Model Stats:
- Params (M): 5.7 - GMACs: 1.1 - Activations (M): 4.1 - Image size: 224 x 224
  • Papers:
- How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
  • Dataset: ImageNet-1k
  • Pretrain Dataset: ImageNet-21k
  • Original: https://github.com/google-research/vision_transformer

Model Usage

Image Classification

python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))


model = timm.create_model('vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)
model = model.eval()


get model specific transforms (normalization, resize)

data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1


top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Image Embeddings

python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))


model = timm.create_model(
    'vit_tiny_patch16_224.augreg_in21k_ft_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()


get model specific transforms (normalization, resize)

data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor


or equivalently (without needing to set num_classes=0)


output = model.forward_features(transforms(img).unsqueeze(0))
output is unpooled, a (1, 197, 192) shaped tensor


output = model.forward_head(output, pre_logits=True)
output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

Citation

bibtex
@article{steiner2021augreg,
  title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},
  author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
  journal={arXiv preprint arXiv:2106.10270},
  year={2021}
}
bibtex
@article{dosovitskiy2020vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}
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/huggingface/pytorch-image-models}}
}
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