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	---
	license: apache-2.0
	library_name: timm
	tags:
	- image-classification
	- timm
	---
	# Model card for cs3darknet_x.c2ns_in1k

	A CS3-DarkNet (Cross-Stage-Partial w/ 3 convolutions) image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.

	Recipe details:
	* Based on [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) `C` recipes w/o repeat-aug and stronger mixup
	* SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping)
	* No stochastic depth used in this `ns` variation of the recipe
	* Cosine LR schedule with warmup


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 35.0
	- GMACs: 8.4
	- Activations (M): 11.3
	- Image size: train = 256 x 256, test = 288 x 288
	- Papers:
	- CSPNet: A New Backbone that can Enhance Learning Capability of CNN: https://arxiv.org/abs/1911.11929
	- YOLOv3: An Incremental Improvement: https://arxiv.org/abs/1804.02767
	- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
	- Original: https://github.com/huggingface/pytorch-image-models

	## 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('cs3darknet_x.c2ns_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(
	'cs3darknet_x.c2ns_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, 80, 128, 128])
	# torch.Size([1, 160, 64, 64])
	# torch.Size([1, 320, 32, 32])
	# torch.Size([1, 640, 16, 16])
	# torch.Size([1, 1280, 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(
	'cs3darknet_x.c2ns_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, 1280, 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).

	## Citation
	```bibtex
	@article{Wang2019CSPNetAN,
	title={CSPNet: A New Backbone that can Enhance Learning Capability of CNN},
	author={Chien-Yao Wang and Hong-Yuan Mark Liao and I-Hau Yeh and Yueh-Hua Wu and Ping-Yang Chen and Jun-Wei Hsieh},
	journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
	year={2019},
	pages={1571-1580}
	}
	```
	```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}}
	}
	```
	```bibtex
	@article{Redmon2018YOLOv3AI,
	title={YOLOv3: An Incremental Improvement},
	author={Joseph Redmon and Ali Farhadi},
	journal={ArXiv},
	year={2018},
	volume={abs/1804.02767}
	}
	```
	```bibtex
	@inproceedings{wightman2021resnet,
	title={ResNet strikes back: An improved training procedure in timm},
	author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
	booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
	}
	```