🚀 focalnet_huge_fl4.ms_in22k 模型卡片
这是一个FocalNet图像分类模型,由论文作者在ImageNet - 22k数据集上进行了预训练,可用于图像分类和特征提取等任务。
🚀 快速开始
本模型可用于图像分类、特征图提取和图像嵌入等任务。以下是具体的使用示例。
✨ 主要特性
- 模型类型:图像分类/特征主干网络
- 模型统计信息:
- 参数数量(M):686.5
- GMACs:118.9
- 激活值数量(M):113.3
- 图像尺寸:224 x 224
- 相关论文:
- Focal Modulation Networks: https://arxiv.org/abs/2203.11926
- 原始代码库:https://github.com/microsoft/FocalNet
- 训练数据集:ImageNet - 22k
💻 使用示例
基础用法 - 图像分类
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()
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))
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
高级用法 - 特征图提取
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()
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))
for o in output:
print(o.shape)
高级用法 - 图像嵌入
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,
)
model = model.eval()
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 = model.forward_features(transforms(img).unsqueeze(0))
output = model.forward_head(output, pre_logits=True)
📚 详细文档
可在timm 模型结果中查看本模型的数据集和运行时指标。
📄 许可证
本项目采用MIT许可证。
📖 引用
@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}}
}