File size: 1,535 Bytes
5bc18fc d435f84 5bc18fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
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)
``` |