sequencer2d_s.in1k 模型卡片

一个 Sequencer2d（基于 LSTM 的）图像分类模型。由论文作者在 ImageNet-1k 数据集上训练而得。

模型细节

• 模型类型：图像分类/特征提取
• 模型统计信息：
  • 参数数量（百万）：27.7
  • GMACs（十亿次乘加数）：5.0
  • 激活值数量（百万）：11.3
  • 图像尺寸：224 x 224
• 论文：
  • Sequencer: Deep LSTM for Image Classification: https://arxiv.org/abs/2205.01972
• 数据集：ImageNet-1k
• 原始论文： https://github.com/okojoalg/sequencer

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('sequencer2d_s.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)

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(
    'sequencer2d_s.in1k',
    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, 32, 32, 192])
    #  torch.Size([1, 16, 16, 384])
    #  torch.Size([1, 16, 16, 384])

    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(
    'sequencer2d_s.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, 16, 16, 384) 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{tatsunami2022sequencer,
  title={Sequencer: Deep LSTM for Image Classification},
  author={Tatsunami, Yuki and Taki, Masato},
  journal={Advances in Neural Information Processing Systems},
  year={2022}
}