caformer_b36.sail_in22k_ft_in1k_384 模型卡片

一种CAFormer（MetaFormer）图像分类模型。由论文作者在ImageNet-22k上进行预训练，并在ImageNet-1k上进行微调。

模型详情

• 模型类型：图像分类 / 特征主干
• 模型统计数据：
  • 参数（百万）：98.8
  • GMACs：72.3
  • 激活数量（百万）：261.8
  • 图像尺寸：384 x 384
• 论文：
  • 视觉任务的元学习基线： https://arxiv.org/abs/2210.13452
• 原始来源： https://github.com/sail-sg/metaformer
• 数据集：ImageNet-1k
• 预训练数据集：ImageNet-22k

模型用途

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('caformer_b36.sail_in22k_ft_in1k_384', 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)

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(
    'caformer_b36.sail_in22k_ft_in1k_384',
    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, 128, 96, 96])
    #  torch.Size([1, 256, 48, 48])
    #  torch.Size([1, 512, 24, 24])
    #  torch.Size([1, 768, 12, 12])

    print(o.shape)

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(
    'caformer_b36.sail_in22k_ft_in1k_384',
    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, 768, 12, 12) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

模型对比

在timm中探索此模型的数据集和运行时指标 model results 。

引用

@article{yu2022metaformer_baselines,
  title={Metaformer baselines for vision},
  author={Yu, Weihao and Si, Chenyang and Zhou, Pan and Luo, Mi and Zhou, Yichen and Feng, Jiashi and Yan, Shuicheng and Wang, Xinchao},
  journal={arXiv preprint arXiv:2210.13452},
  year={2022}
}