Vitamin XL 384px

🚀 ViTamin-XL-336px模型卡片

ViTamin的官方Hugging Face模型，來自以下2024年CVPR論文：

ViTamin: Design Scalable Vision Models in the Vision - language Era。
✨  Jieneng Chen，Qihang Yu，Xiaohui Shen，Alan Yuille 和 Liang - Chieh Chen
🏠  約翰霍普金斯大學，字節跳動

🚀 快速開始

可以使用transformers.AutoModel從Hugging Face加載模型：

import torch
import open_clip
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModel.from_pretrained(
    'jienengchen/ViTamin-XL-384px',
    trust_remote_code=True).to(device).eval()

image = Image.open('./image.png').convert('RGB')
image_processor = CLIPImageProcessor.from_pretrained('jienengchen/ViTamin-XL-384px')

pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()

tokenizer = open_clip.get_tokenizer('hf-hub:laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K')
text = tokenizer(["a photo of vitamin", "a dog", "a cat"]).to(device)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features, text_features, logit_scale = model(pixel_values, text)
    text_probs = (100.0 * image_features @ text_features.to(torch.float).T).softmax(dim=-1)

print("Label probs:", text_probs)

✨ 主要特性

基於DataComp - 1B的CLIP預訓練主要結果

下游任務主要結果

開放詞彙檢測

開放詞彙分割

注意：全景數據集（ADE、CityScapes、MV）使用PQ指標。語義數據集（A - 150、A - 847、PC - 459、PC - 59、PAS - 21）使用mIoU指標。

大型多模態模型

📄 許可證

本項目採用MIT許可證。

📚 詳細文檔

引用ViTamin

@inproceedings{chen2024vitamin,
  title={ViTamin: Design Scalable Vision Models in the Vision-language Era},
  author={Chen, Jieneng and Yu, Qihang and Shen, Xiaohui and Yuille, ALan and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}