Focalnet Huge Fl4.ms In22k

🚀 FocalNet Huge FL4 MS IN22K

A FocalNet image classification model pretrained on ImageNet-22k by paper authors, used for image feature extraction.

🚀 Quick Start

This is a FocalNet image classification model, pretrained on ImageNet-22k by the paper authors.

✨ Features

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 686.5
  • GMACs: 118.9
  • Activations (M): 113.3
  • Image size: 224 x 224
• Papers:
  • Focal Modulation Networks: https://arxiv.org/abs/2203.11926
• Original: https://github.com/microsoft/FocalNet
• Dataset: ImageNet-22k

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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('focalnet_huge_fl4.ms_in22k', 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)

Advanced Usage

Feature Map Extraction

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(
    'focalnet_huge_fl4.ms_in22k',
    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. for focalnet_base_srf: 
    #  torch.Size([1, 128, 56, 56])
    #  torch.Size([1, 256, 28, 28])
    #  torch.Size([1, 512, 14, 14])
    #  torch.Size([1, 1024, 7, 7])
    print(o.shape)

Image Embeddings

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(
    'focalnet_huge_fl4.ms_in22k',
    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 (ie.e a (batch_size, num_features, H, W) tensor)

output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor

📚 Documentation

Explore the dataset and runtime metrics of this model in timm model results.

📄 License

This project is licensed under the MIT license.

📚 Citation

@misc{yang2022focal,
  title={Focal Modulation Networks}, 
  author={Jianwei Yang and Chunyuan Li and Xiyang Dai and Jianfeng Gao},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2022}
}

@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}}
}