Update model config and README

ce15783 about 1 year ago

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	---
	tags:
	- image-classification
	- timm
	library_name: timm
	license: apache-2.0
	datasets:
	- imagenet-1k
	- laion-2b
	---
	# Model card for vit_base_patch16_clip_224.laion2b_ft_in1k

	A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 86.6
	- GMACs: 16.9
	- Activations (M): 16.5
	- Image size: 224 x 224
	- Papers:
	- OpenCLIP: https://github.com/mlfoundations/open_clip
	- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
	- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
	- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
	- Dataset: ImageNet-1k
	- Pretrain Dataset:
	- LAION-2B

	## 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_base_patch16_clip_224.laion2b_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_base_patch16_clip_224.laion2b_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, 768) 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](https://github.com/huggingface/pytorch-image-models/tree/main/results).

	## Citation
	```bibtex
	@software{ilharco_gabriel_2021_5143773,
	author = {Ilharco, Gabriel and
	Wortsman, Mitchell and
	Wightman, Ross and
	Gordon, Cade and
	Carlini, Nicholas and
	Taori, Rohan and
	Dave, Achal and
	Shankar, Vaishaal and
	Namkoong, Hongseok and
	Miller, John and
	Hajishirzi, Hannaneh and
	Farhadi, Ali and
	Schmidt, Ludwig},
	title = {OpenCLIP},
	month = jul,
	year = 2021,
	note = {If you use this software, please cite it as below.},
	publisher = {Zenodo},
	version = {0.1},
	doi = {10.5281/zenodo.5143773},
	url = {https://doi.org/10.5281/zenodo.5143773}
	}
	```
	```bibtex
	@article{cherti2022reproducible,
	title={Reproducible scaling laws for contrastive language-image learning},
	author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
	journal={arXiv preprint arXiv:2212.07143},
	year={2022}
	}
	```
	```bibtex
	@inproceedings{schuhmann2022laionb,
	title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
	author={Christoph Schuhmann and
	Romain Beaumont and
	Richard Vencu and
	Cade W Gordon and
	Ross Wightman and
	Mehdi Cherti and
	Theo Coombes and
	Aarush Katta and
	Clayton Mullis and
	Mitchell Wortsman and
	Patrick Schramowski and
	Srivatsa R Kundurthy and
	Katherine Crowson and
	Ludwig Schmidt and
	Robert Kaczmarczyk and
	Jenia Jitsev},
	booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
	year={2022},
	url={https://openreview.net/forum?id=M3Y74vmsMcY}
	}
	```
	```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}}
	}
	```