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.

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:

pip install torch torchvision transformers

Code Example:

Here is an example Python code snippet for using the FAL model to classify a video:

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 project is licensed under the SVECTOR Proprietary License. Refer to the LICENSE file for more details.

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:

@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].

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