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---
license: cc-by-nc-4.0
tags:
- vision
- video-classification
language:
- en
pipeline_tag: video-classification
---
# FAL - Framework For Automated Labeling Of Videos (FALVideoClassifier)
FAL (Framework for Automated Labeling Of Videos) is a custom video classification model developed by **SVECTOR** and fine-tuned on the **FAL-500** dataset. This model is designed for efficient video understanding and classification, leveraging state-of-the-art video processing techniques.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6631e2b06d207536a4651738/Sf9tEMK8989JpQorvokT_.png" alt="Demo" width="560">
## Model Overview
This model, referred to as `FALVideoClassifier`, fine-tuned on **FAL-500** Dataset, and optimized for automated video labeling tasks. It is capable of classifying a video into one of the 5
00 possible labels from the FAL-500 dataset.
This model was developed by **SVECTOR** as part of our initiative to advance automated video understanding and classification technologies.
## Intended Uses & Limitations
This model is designed for video classification tasks, and you can use it to classify videos into one of the 500 classes from the FAL-500 dataset. Please note that the model was trained on **FAL-500** and may not perform as well on datasets that significantly differ from this.
### Intended Use:
- Automated video labeling
- Video content classification
- Research in video understanding and machine learning
### Limitations:
- Only trained on FAL-500
- May not generalize well to out-of-domain videos without further fine-tuning
- Requires videos to be pre-processed (such as resizing frames, normalization, etc.)
## How to Use
To use this model for video classification, follow these steps:
### Installation:
Ensure you have the necessary dependencies installed:
```bash
pip install torch torchvision transformers
```
### Code Example:
Here is an example Python code snippet for using the FAL model to classify a video:
```python
from transformers import AutoImageProcessor, FALVideoClassifierForVideoClassification
import numpy as np
import torch
# Simulating a sample video (8 frames of size 224x224 with 3 color channels)
video = list(np.random.randn(8, 3, 224, 224)) # 8 frames, each of size 224x224 with RGB channels
# Load the image processor and model
processor = AutoImageProcessor.from_pretrained("SVECTOR-CORPORATION/FAL")
model = FALVideoClassifierForVideoClassification.from_pretrained("SVECTOR-CORPORATION/FAL")
# Pre-process the video input
inputs = processor(video, return_tensors="pt")
# Run inference with no gradient calculation (evaluation mode)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Find the predicted class (highest logit)
predicted_class_idx = logits.argmax(-1).item()
# Output the predicted label
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
### Model Details:
- **Model Name**: `FALVideoClassifier`
- **Dataset Used**: FAL-S500
- **Input Size**: 8 frames of size 224x224 with 3 color channels (RGB)
### Configuration:
The `FALVideoClassifier` uses the following hyperparameters:
- `num_frames`: Number of frames in the video (e.g., 8)
- `num_labels`: The number of possible video classes (500 for FAL-500)
- `hidden_size`: Hidden size for transformer layers (768)
- `attention_probs_dropout_prob`: Dropout probability for attention layers (0.0)
- `hidden_dropout_prob`: Dropout probability for the hidden layers (0.0)
- `drop_path_rate`: Dropout rate for stochastic depth (0.0)
### Preprocessing:
Before feeding videos into the model, ensure the frames are properly pre-processed:
- Resize frames to `224x224`
- Normalize pixel values (use the processor from the model, as shown in the code)
## License
This model is licensed under the **CC-BY-NC-4.0** license, which means it can be used for non-commercial purposes with proper attribution.
## Citation
If you use this model in your research or projects, please cite the following:
```bibtex
@misc{svector2024fal,
title={FAL - Framework For Automated Labeling Of Videos (FALVideoClassifier)},
author={SVECTOR},
year={2024},
url={https://www.svector.co.in},
}
```
## Contact
For any inquiries regarding this model or its implementation, you can contact the SVECTOR team at [email protected].
--- |