Timesformer Hr Finetuned K600

🚀 TimeSformer（高分辨率變體，在Kinetics - 600上微調）

TimeSformer是一個在Kinetics - 600數據集上預訓練的模型。它由Tong等人在論文TimeSformer: Is Space - Time Attention All You Need for Video Understanding?中提出，並首次在此倉庫發佈。

聲明：發佈TimeSformer的團隊並未為此模型撰寫模型卡片，此模型卡片由fcakyon撰寫。

🚀 快速開始

你可以使用該原始模型將視頻分類為600種可能的Kinetics - 600標籤之一。

💻 使用示例

基礎用法

from transformers import AutoImageProcessor, TimesformerForVideoClassification
import numpy as np
import torch

video = list(np.random.randn(16, 3, 448, 448))

processor = AutoImageProcessor.from_pretrained("fcakyon/timesformer-hr-finetuned-k600")
model = TimesformerForVideoClassification.from_pretrained("fcakyon/timesformer-hr-finetuned-k600")

inputs = processor(images=video, return_tensors="pt")

with torch.no_grad():
  outputs = model(**inputs)
  logits = outputs.logits

predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

如需更多代碼示例，請參考文檔。

📚 詳細文檔

BibTeX引用和引用信息

@inproceedings{bertasius2021space,
  title={Is Space-Time Attention All You Need for Video Understanding?},
  author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
  booktitle={International Conference on Machine Learning},
  pages={813--824},
  year={2021},
  organization={PMLR}
}

📄 許可證

本項目採用CC - BY - NC - 4.0許可證。