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	---
	tags:
	- image-classification
	- timm
	library_name: timm
	license: apache-2.0
	datasets:
	- laion-2b
	- timm/imagenet-12k-wds
	---
	# Model card for convnext_xxlarge.clip_laion2b_soup_ft_in12k

	A ConvNeXt image classification model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION and fine-tuned on ImageNet-12k by Ross Wightman.

	Please see related OpenCLIP model cards for more details on pretrain:
	* https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
	* https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg
	* https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
	* https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 879.7
	- GMACs: 198.1
	- Activations (M): 124.5
	- Image size: 256 x 256
	- Papers:
	- OpenCLIP: https://github.com/mlfoundations/open_clip
	- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
	- @: m
	- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
	- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
	- Original: https://github.com/mlfoundations/open_clip
	- Pretrain Dataset: LAION-2B
	- Dataset: ImageNet-12k

	## 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('convnext_xxlarge.clip_laion2b_soup_ft_in12k', 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)
	```

	### Feature Map Extraction
	```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(
	'convnext_xxlarge.clip_laion2b_soup_ft_in12k',
	pretrained=True,
	features_only=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

	for o in output:
	# print shape of each feature map in output
	# e.g.:
	# torch.Size([1, 384, 64, 64])
	# torch.Size([1, 768, 32, 32])
	# torch.Size([1, 1536, 16, 16])
	# torch.Size([1, 3072, 8, 8])

	print(o.shape)
	```

	### 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(
	'convnext_xxlarge.clip_laion2b_soup_ft_in12k',
	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, 3072, 8, 8) 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).

	All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

	\| model \|top1 \|top5 \|img_size\|param_count\|gmacs \|macts \|samples_per_sec\|batch_size\|
	\|------------------------------------------------------------------------------------------------------------------------------\|------\|------\|--------\|-----------\|------\|------\|---------------\|----------\|
	\| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) \|88.848\|98.742\|512 \|660.29 \|600.81\|413.07\|28.58 \|48 \|
	\| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) \|88.668\|98.738\|384 \|660.29 \|337.96\|232.35\|50.56 \|64 \|
	\| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) \|88.612\|98.704\|256 \|846.47 \|198.09\|124.45\|122.45 \|256 \|
	\| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) \|88.312\|98.578\|384 \|200.13 \|101.11\|126.74\|196.84 \|256 \|
	\| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) \|88.196\|98.532\|384 \|197.96 \|101.1 \|126.74\|128.94 \|128 \|
	\| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) \|87.968\|98.47 \|320 \|200.13 \|70.21 \|88.02 \|283.42 \|256 \|
	\| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) \|87.75 \|98.556\|384 \|350.2 \|179.2 \|168.99\|124.85 \|192 \|
	\| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) \|87.646\|98.422\|384 \|88.72 \|45.21 \|84.49 \|209.51 \|256 \|
	\| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) \|87.476\|98.382\|384 \|197.77 \|101.1 \|126.74\|194.66 \|256 \|
	\| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) \|87.344\|98.218\|256 \|200.13 \|44.94 \|56.33 \|438.08 \|256 \|
	\| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) \|87.26 \|98.248\|224 \|197.96 \|34.4 \|43.13 \|376.84 \|256 \|
	\| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) \|87.138\|98.212\|384 \|88.59 \|45.21 \|84.49 \|365.47 \|256 \|
	\| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) \|87.002\|98.208\|224 \|350.2 \|60.98 \|57.5 \|368.01 \|256 \|
	\| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) \|86.796\|98.264\|384 \|88.59 \|45.21 \|84.49 \|366.54 \|256 \|
	\| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) \|86.74 \|98.022\|224 \|88.72 \|15.38 \|28.75 \|624.23 \|256 \|
	\| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) \|86.636\|98.028\|224 \|197.77 \|34.4 \|43.13 \|581.43 \|256 \|
	\| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) \|86.504\|97.97 \|384 \|88.59 \|45.21 \|84.49 \|368.14 \|256 \|
	\| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) \|86.344\|97.97 \|256 \|88.59 \|20.09 \|37.55 \|816.14 \|256 \|
	\| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) \|86.256\|97.75 \|224 \|660.29 \|115.0 \|79.07 \|154.72 \|256 \|
	\| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) \|86.182\|97.92 \|384 \|50.22 \|25.58 \|63.37 \|516.19 \|256 \|
	\| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) \|86.154\|97.68 \|256 \|88.59 \|20.09 \|37.55 \|819.86 \|256 \|
	\| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) \|85.822\|97.866\|224 \|88.59 \|15.38 \|28.75 \|1037.66 \|256 \|
	\| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) \|85.778\|97.886\|384 \|50.22 \|25.58 \|63.37 \|518.95 \|256 \|
	\| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) \|85.742\|97.584\|224 \|197.96 \|34.4 \|43.13 \|375.23 \|256 \|
	\| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) \|85.174\|97.506\|224 \|50.22 \|8.71 \|21.56 \|1474.31 \|256 \|
	\| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) \|85.118\|97.608\|384 \|28.59 \|13.14 \|39.48 \|856.76 \|256 \|
	\| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) \|85.112\|97.63 \|384 \|28.64 \|13.14 \|39.48 \|491.32 \|256 \|
	\| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) \|84.874\|97.09 \|224 \|88.72 \|15.38 \|28.75 \|625.33 \|256 \|
	\| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) \|84.562\|97.394\|224 \|50.22 \|8.71 \|21.56 \|1478.29 \|256 \|
	\| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) \|84.282\|96.892\|224 \|197.77 \|34.4 \|43.13 \|584.28 \|256 \|
	\| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) \|84.186\|97.124\|224 \|28.59 \|4.47 \|13.44 \|2433.7 \|256 \|
	\| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) \|84.084\|97.14 \|384 \|28.59 \|13.14 \|39.48 \|862.95 \|256 \|
	\| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) \|83.894\|96.964\|224 \|28.64 \|4.47 \|13.44 \|1452.72 \|256 \|
	\| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) \|83.82 \|96.746\|224 \|88.59 \|15.38 \|28.75 \|1054.0 \|256 \|
	\| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) \|83.37 \|96.742\|384 \|15.62 \|7.22 \|24.61 \|801.72 \|256 \|
	\| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) \|83.142\|96.434\|224 \|50.22 \|8.71 \|21.56 \|1464.0 \|256 \|
	\| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) \|82.92 \|96.284\|224 \|28.64 \|4.47 \|13.44 \|1425.62 \|256 \|
	\| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) \|82.898\|96.616\|224 \|28.59 \|4.47 \|13.44 \|2480.88 \|256 \|
	\| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) \|82.282\|96.344\|224 \|15.59 \|2.46 \|8.37 \|3926.52 \|256 \|
	\| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) \|82.216\|95.852\|224 \|28.59 \|4.47 \|13.44 \|2529.75 \|256 \|
	\| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) \|82.066\|95.854\|224 \|28.59 \|4.47 \|13.44 \|2346.26 \|256 \|
	\| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) \|82.03 \|96.166\|224 \|15.62 \|2.46 \|8.37 \|2300.18 \|256 \|
	\| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) \|81.83 \|95.738\|224 \|15.62 \|2.46 \|8.37 \|2321.48 \|256 \|
	\| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) \|80.866\|95.246\|224 \|15.65 \|2.65 \|9.38 \|3523.85 \|256 \|
	\| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) \|80.768\|95.334\|224 \|15.59 \|2.46 \|8.37 \|3915.58 \|256 \|
	\| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) \|80.304\|95.072\|224 \|9.07 \|1.37 \|6.1 \|3274.57 \|256 \|
	\| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) \|79.526\|94.558\|224 \|9.05 \|1.37 \|6.1 \|5686.88 \|256 \|
	\| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) \|79.522\|94.692\|224 \|9.06 \|1.43 \|6.5 \|5422.46 \|256 \|
	\| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) \|78.488\|93.98 \|224 \|5.23 \|0.79 \|4.57 \|4264.2 \|256 \|
	\| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) \|77.86 \|93.83 \|224 \|5.23 \|0.82 \|4.87 \|6910.6 \|256 \|
	\| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) \|77.454\|93.68 \|224 \|5.22 \|0.79 \|4.57 \|7189.92 \|256 \|
	\| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) \|76.664\|93.044\|224 \|3.71 \|0.55 \|3.81 \|4728.91 \|256 \|
	\| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) \|75.88 \|92.846\|224 \|3.7 \|0.58 \|4.11 \|7963.16 \|256 \|
	\| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) \|75.664\|92.9 \|224 \|3.7 \|0.55 \|3.81 \|8439.22 \|256 \|

	## 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
	@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
	i
	```
	```bibtex
	@inproceedings{Radford2021LearningTV,
	title={Learning Transferable Visual Models From Natural Language Supervision},
	author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
	booktitle={ICML},
	year={2021}
	}
	```
	```bibtex
	@article{liu2022convnet,
	author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
	title = {A ConvNet for the 2020s},
	journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2022},
	}
	```