Eva02 Large Patch14 224.mim In22k

🚀 eva02_large_patch14_224.mim_in22k模型卡片

EVA02是一個特徵/表徵模型，由論文作者使用掩碼圖像建模（以EVA - CLIP作為MIM教師）在ImageNet - 22k上進行預訓練。

EVA - 02模型是帶有均值池化、SwiGLU、旋轉位置嵌入（ROPE）的視覺變換器，並且在MLP（基礎版和大型版）中使用了額外的LN。

注意：為了與其他模型保持一致，timm檢查點為float32格式。在某些情況下，原始檢查點為float16或bfloat16格式，若有需要可查看原始版本。

🚀 快速開始

本模型可用於圖像分類和特徵提取，以下是使用示例。

✨ 主要特性

• 基於視覺變換器架構，結合了均值池化、SwiGLU、旋轉位置嵌入（ROPE）等技術。
• 在ImageNet - 22k上使用掩碼圖像建模進行預訓練。

📦 安裝指南

文檔未提及安裝步驟，跳過此章節。

💻 使用示例

基礎用法

圖像分類

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('eva02_large_patch14_224.mim_in22k', 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)

圖像嵌入

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(
    'eva02_large_patch14_224.mim_in22k',
    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, 257, 1024) shaped tensor

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

📚 詳細文檔

模型詳情

模型比較

可在timm 模型結果中查看此模型的數據集和運行時指標。

引用信息

@article{EVA02,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.11331},
  year={2023}
}

@article{EVA-CLIP,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.15389},
  year={2023}
}

@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}}
}

📄 許可證

本項目採用MIT許可證。