许可协议: cc-by-nc-4.0 模型库: timm 标签:

• 图像特征提取
• timm
• transformers

convnextv2_base.fcmae 模型卡片

一个基于ConvNeXt-V2的自监督特征表示模型。采用全卷积掩码自编码器框架(FCMAE)进行预训练。该模型不包含预训练头部，仅适用于微调或特征提取任务。

模型详情

• 模型类型: 图像分类/特征主干网络
• 模型统计:
  • 参数量(M): 87.7
  • GMACs计算量: 15.4
  • 激活值(M): 28.8
  • 输入图像尺寸: 224×224
• 相关论文:
  • ConvNeXt V2: 与掩码自编码器协同设计与扩展卷积网络: https://arxiv.org/abs/2301.00808
• 原始实现: https://github.com/facebookresearch/ConvNeXt-V2
• 预训练数据集: ImageNet-1k

使用示例

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('convnextv2_base.fcmae', pretrained=True)
model = model.eval()

# 获取模型特定的数据转换配置(标准化、尺寸调整)
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))  # 将单张图像扩展为batch_size=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(
    'convnextv2_base.fcmae',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# 获取模型特定的数据转换配置
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))  # 将单张图像扩展为batch_size=1

for o in output:
    # 打印输出中每个特征图的形状
    # 例如:
    #  torch.Size([1, 128, 56, 56])
    #  torch.Size([1, 256, 28, 28])
    #  torch.Size([1, 512, 14, 14])
    #  torch.Size([1, 1024, 7, 7])

    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(
    'convnextv2_base.fcmae',
    pretrained=True,
    num_classes=0,  # 移除分类器nn.Linear层
)
model = model.eval()

# 获取模型特定的数据转换配置
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))  # 输出为(batch_size, num_features)形状张量

# 或等效方式(无需设置num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# 输出为未池化的(1, 1024, 7, 7)形状张量

output = model.forward_head(output, pre_logits=True)
# 输出为(1, num_features)形状张量

模型对比

在timm的模型结果中查看该模型的数据集和运行时指标。

所有计时数据均来自RTX 3090显卡上使用AMP加速的PyTorch 1.13 eager模式。

引用文献

@article{Woo2023ConvNeXtV2,
  title={ConvNeXt V2: 与掩码自编码器协同设计与扩展卷积网络},
  author={Woo, Sanghyun and Debnath, Shoubhik and Hu, Ronghang and Chen, Xinlei and Liu, Zhuang and Kweon, In So and Xie, Saining},
  year={2023},
  journal={arXiv预印本 arXiv:2301.00808},
}

@misc{rw2019timm,
  author = {Wightman, Ross},
  title = {PyTorch图像模型库},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub仓库},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}