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.
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:
python
1from transformers import AutoImageProcessor, FALVideoClassifierForVideoClassification
2import numpy as np
3import torch
45# Simulating a sample video (8 frames of size 224x224 with 3 color channels)6video =list(np.random.randn(8,3,224,224))# 8 frames, each of size 224x224 with RGB channels78# Load the image processor and model9processor = AutoImageProcessor.from_pretrained("SVECTOR-CORPORATION/FAL")10model = FALVideoClassifierForVideoClassification.from_pretrained("SVECTOR-CORPORATION/FAL")1112# Pre-process the video input13inputs = processor(video, return_tensors="pt")1415# Run inference with no gradient calculation (evaluation mode)16with torch.no_grad():17 outputs = model(**inputs)18 logits = outputs.logits
1920# Find the predicted class (highest logit)21predicted_class_idx = logits.argmax(-1).item()2223# Output the predicted label24print("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:
bibtex
1@misc{svector2024fal,
2 title={FAL - Framework For Automated Labeling Of Videos (FALVideoClassifier)},
3 author={SVECTOR},
4 year={2024},
5 url={https://www.svector.co.in},
67}
8
