timesformer hr finetuned k600 LLM Model

Introduction

The TimeSformer model, fine-tuned on Kinetics-600, is designed for video classification tasks. It leverages space-time attention mechanisms to classify videos into one of the 600 labels from the Kinetics-600 dataset. This model was introduced in the paper "TimeSformer: Is Space-Time Attention All You Need for Video Understanding?" by Tong et al.

Architecture

TimeSformer is a transformer-based model specifically tailored for video understanding. It applies space-time attention across video frames, effectively capturing temporal and spatial features for video classification.

Training

The TimeSformer model was pre-trained on the Kinetics-600 dataset, a large-scale video dataset consisting of 600 action classes. The fine-tuning process involved adjusting the model parameters to enhance its performance on this specific dataset.

Guide: Running Locally

To run TimeSformer locally, follow these steps:

Install the Transformers library: Ensure you have the Transformers library installed via pip:
```
pip install transformers
```

Import necessary libraries: Use PyTorch and NumPy:

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

Prepare video input: Prepare your video data in the required format:
```
video = list(np.random.randn(16, 3, 448, 448))
```

Load the model and processor:

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

Process the input and perform inference:

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])

For optimal performance, consider using cloud GPUs from providers like AWS, GCP, or Azure.

License

The TimeSformer model is released under the CC BY-NC 4.0 license, allowing for non-commercial use with appropriate credit.

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