Update model config and README

76a7dc3 about 1 year ago

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	---
	tags:
	- image-classification
	- timm
	library_name: timm
	license: apache-2.0
	datasets:
	- imagenet-1k
	---
	# Model card for lcnet_050.ra2_in1k

	A LCNet image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.

	Recipe details:
	* RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476).
	* RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
	* Step (exponential decay w/ staircase) LR schedule with warmup


	## Model Details
	- Model Type: Image classification / feature backbone
	- Model Stats:
	- Params (M): 1.9
	- GMACs: 0.0
	- Activations (M): 1.3
	- Image size: 224 x 224
	- Papers:
	- PP-LCNet: A Lightweight CPU Convolutional Neural Network: https://arxiv.org/abs/2109.15099
	- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
	- Dataset: ImageNet-1k
	- 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('lcnet_050.ra2_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(
	'lcnet_050.ra2_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, 16, 112, 112])
	# torch.Size([1, 32, 56, 56])
	# torch.Size([1, 64, 28, 28])
	# torch.Size([1, 128, 14, 14])
	# torch.Size([1, 256, 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(
	'lcnet_050.ra2_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, 256, 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).

	## Citation
	```bibtex
	@article{cui2021pp,
	title={PP-LCNet: A lightweight CPU convolutional neural network},
	author={Cui, Cheng and Gao, Tingquan and Wei, Shengyu and Du, Yuning and Guo, Ruoyu and Dong, Shuilong and Lu, Bin and Zhou, Ying and Lv, Xueying and Liu, Qiwen and others},
	journal={arXiv preprint arXiv:2109.15099},
	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}}
	}
	```
	```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}
	}
	```