bat_resnext26ts.ch_in1k的模型卡片

一种BAT-ResNeXt图像分类模型（具有'分组双线性注意力变换'的ResNeXt）。该模型具有分层的3层干细胞和SiLU激活函数。由Ross Wightman在timm中使用ImageNet-1k进行训练。

此模型架构使用timm的灵活 BYOBNet (Bring-Your-Own-Blocks Network) 实现。

BYOBNet允许配置：

• block / stage布局
• 干细胞布局
• 输出步幅（膨胀）
• 激活和归一化层
• 通道和空间/自注意力层

...还包括timm中许多其他架构常见的功能，包括：

• 随机深度
• 梯度检查点
• 分层LR衰减
• 按阶段进行特征提取

模型详细信息

• 模型类型：图像分类/特征骨干
• 模型统计：
  • 参数（M）：10.7
  • GMACs：2.5
  • 激活（M）：12.5
  • 图像尺寸：256 x 256
• 论文：
  • 非局部神经网络与分组双线性注意力变换： https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html
  • 聚合残差转换用于深度神经网络： https://arxiv.org/abs/1611.05431
• 数据集：ImageNet-1k
• 原始链接： https://github.com/huggingface/pytorch-image-models

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('bat_resnext26ts.ch_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(
    'bat_resnext26ts.ch_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, 64, 128, 128])
    #  torch.Size([1, 256, 64, 64])
    #  torch.Size([1, 512, 32, 32])
    #  torch.Size([1, 1024, 16, 16])
    #  torch.Size([1, 2048, 8, 8])

    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(
    'bat_resnext26ts.ch_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, 2048, 8, 8) shaped tensor

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

模型比较

在timm model results 中探索该模型的数据集和运行时指标。

引用

@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}}
}

@InProceedings{Chi_2020_CVPR,
  author = {Chi, Lu and Yuan, Zehuan and Mu, Yadong and Wang, Changhu},
  title = {Non-Local Neural Networks With Grouped Bilinear Attentional Transforms},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}

@article{Xie2016,
  title={Aggregated Residual Transformations for Deep Neural Networks},
  author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He},
  journal={arXiv preprint arXiv:1611.05431},
  year={2016}
}