convit_small.fb_in1k的模型卡片

一个ConViT图像分类模型。由论文作者在ImageNet-1k上进行训练。

模型详情

• 模型类型：图像分类/特征主干
• 模型统计数据：
  • 参数数量（百万）：27.8
  • GMACs：5.8
  • 激活数量（百万）：17.9
  • 图像尺寸：224 x 224
• 相关论文：
  • ConViT：通过软卷积引导来改进Vision Transformers： https://arxiv.org/abs/2103.10697
• 数据集：Imagenet-1k
• 原始论文： https://github.com/facebookresearch/convit

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('convit_small.fb_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)

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(
    'convit_small.fb_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, 197, 432) shaped tensor

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

模型比较

在timm中探索该模型的数据集和运行时指标： model results

引用

@article{d2021convit,
  title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
  author={d'Ascoli, St{'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
  journal={arXiv preprint arXiv:2103.10697},
  year={2021}
}