Fix library_name.

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	---
	license: apache-2.0
	library_name: timm
	tags:
	- image-classification
	- timm
	datasets:
	- imagenet-1k
	---
	# Model card for convnext_large.fb_in1k

	A ConvNeXt image classification model. Pretrained on ImageNet-1k by paper authors.


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 197.8
	- GMACs: 34.4
	- Activations (M): 43.1
	- Image size: train = 224 x 224, test = 288 x 288
	- Papers:
	- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
	- Original: https://github.com/facebookresearch/ConvNeXt
	- Dataset: ImageNet-1k

	## 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_large.fb_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)
	```

	### 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_large.fb_in1k',
	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, 192, 56, 56])
	# torch.Size([1, 384, 28, 28])
	# torch.Size([1, 768, 14, 14])
	# torch.Size([1, 1536, 7, 7])

	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_large.fb_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, 1536, 7, 7) 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
	@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},
	}
	```
	```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}}
	}
	```