README.md

---
language: en
tags:
- video-classification
license: apache-2.0
datasets:
- ucf101
metrics:
- accuracy
- top-5-accuracy
pipeline_tag: video-classification
model-index:
- name: i3d-kinetics-400
  results:
  - task:
      type: video-classification
      name: Video Classification
    dataset:
      name: UCF101
      type: ucf101
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.95
      - name: Top-5 Accuracy
        type: top-5-accuracy
        value: 0.95
---

# I3D Kinetics-400

This model is a fine-tuned version of the Inflated 3D Convnet model for action recognition, trained on the Kinetics-400 dataset.

## Model Description

The I3D (Inflated 3D Convnet) model is designed for video classification tasks. It extends 2D convolutions to 3D, enabling the model to capture spatiotemporal features from video frames.

## Intended Uses

The model can be used for action recognition in videos. It is particularly suited for tasks involving the classification of human activities.

## Training Data

The model was fine-tuned on the UCF101 dataset, which consists of 13,320 videos belonging to 101 action categories.

## Performance

The model achieves an accuracy of 90% and a top-5 accuracy of 95% on the UCF101 test set.

## Example Usage

```python
from transformers import pipeline

model = pipeline("video-classification", model="Mouwiya/i3d-kinetics-400")

# Example video path
video_path = "path_to_your_video.mp4"

# Perform video classification
results = model(video_path)
print(results)
```