F A L

Introduction

FAL (Framework for Automated Labeling Of Videos) is a video classification model developed by SVECTOR, fine-tuned on the FAL-500 dataset. It is engineered for efficient video understanding and classification, using advanced video processing techniques.

Architecture

The FALVideoClassifier is optimized for automated video labeling tasks. It classifies videos into one of the 500 possible labels from the FAL-500 dataset. The model incorporates a transformer-based architecture with hyperparameters like 768 hidden size, dropout probabilities set to 0.0, and a drop path rate of 0.0.

Training

The model is specifically trained on the FAL-500 dataset, which may limit its performance on significantly different datasets. Preprocessed video frames are required for optimal performance, with resizing to 224x224 and normalization of pixel values.

Guide: Running Locally

1. Install Dependencies:

   pip install torch torchvision transformers
   

2. Use the Model:

   from transformers import AutoImageProcessor, FALVideoClassifierForVideoClassification
   import numpy as np
   import torch
   
   # Simulating a sample video
   video = list(np.random.randn(8, 3, 224, 224))
   
   # Load the processor and model
   processor = AutoImageProcessor.from_pretrained("SVECTOR-CORPORATION/FAL")
   model = FALVideoClassifierForVideoClassification.from_pretrained("SVECTOR-CORPORATION/FAL")
   
   # Pre-process and classify
   inputs = processor(video, return_tensors="pt")
   with torch.no_grad():
       outputs = model(**inputs)
       logits = outputs.logits
   predicted_class_idx = logits.argmax(-1).item()
   print("Predicted class:", model.config.id2label[predicted_class_idx])
   

3. Cloud GPUs: For improved performance, consider using cloud GPU services like AWS EC2, Google Cloud, or Azure.

License

This model is licensed under the CC-BY-NC-4.0 license, allowing use for non-commercial purposes with proper attribution. For further details, refer to the license file.