Samvit Base Patch16.sa1b

🚀 samvit_base_patch16.sa1b模型卡片

這是一個Segment-Anything Vision Transformer (SAM ViT)圖像特徵模型（注意：用於提取特徵和微調，不包含分割頭）。該模型由論文作者在SA - 1B數據集上進行預訓練以用於分割任務，並使用MAE的權重進行初始化。

🚀 快速開始

本模型是一個基於Transformer架構的圖像特徵提取模型，可用於圖像分類和獲取圖像嵌入等任務。

✨ 主要特性

• 模型類型：圖像分類/特徵骨幹網絡
• 模型統計信息：
  • 參數數量（M）：89.7
  • GMACs：486.4
  • 激活值數量（M）：1343.3
  • 圖像尺寸：1024 x 1024
• 相關論文：
  • Segment Anything
  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• 原始代碼庫：https://github.com/facebookresearch/segment - anything
• 預訓練數據集：SA - 1B

📦 安裝指南

文檔未提及安裝步驟，若需使用該模型，可參考timm庫的官方安裝說明。

💻 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('samvit_base_patch16.sa1b', 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(
    'samvit_base_patch16.sa1b',
    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, 256, 64, 64) shaped tensor

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

📚 詳細文檔

可在timm 模型結果中探索該模型的數據集和運行時指標。

📄 許可證

本項目採用Apache - 2.0許可證。

📚 引用

@article{kirillov2023segany,
  title={Segment Anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

@article{dosovitskiy2020vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}

@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}}
}