twins_pcpvt_large.in1k的模型卡

一个Twins-PCPVT图像分类模型。由论文作者在ImageNet-1k上训练。

模型细节

• 模型类型：图像分类/特征骨干
• 模型统计数据：
  • 参数数目 (M)：61.0
  • GMACs：9.8
  • 激活值数目 (M)：35.8
  • 图像尺寸：224 x 224
• 论文：
  • Twins: Revisiting the Design of Spatial Attention in Vision Transformers: https://arxiv.org/abs/2104.13840
• 数据集： ImageNet-1k
• 原始论文： https://github.com/Meituan-AutoML/Twins

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('twins_pcpvt_large.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(
    'twins_pcpvt_large.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, 49, 512) 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

@inproceedings{chu2021Twins,
    title={Twins: Revisiting the Design of Spatial Attention in Vision Transformers},
    author={Xiangxiang Chu and Zhi Tian and Yuqing Wang and Bo Zhang and Haibing Ren and Xiaolin Wei and Huaxia Xia and Chunhua Shen},
    booktitle={NeurIPS 2021},
    url={https://openreview.net/forum?id=5kTlVBkzSRx},
    year={2021}
}