FAL-1.5: Enhanced Video Classification

FAL-1.5 brings a major leap in SVECTOR’s video classification capabilities. Built on top of the original FAL architecture, it now delivers sharper labeling, smarter attention routing, and significantly improved inference speed.

What’s New in FAL-1.5

FAL-1.5 introduces several key enhancements over its predecessor, FAL-1, making it a more powerful tool for video classification tasks. These improvements include:

• Enhanced Attention Mechanisms: More accurate scene understanding with improved frame-token alignment
• Faster Inference: Optimized processing pipeline delivering up to 20% speedup on modern GPUs
• Refined Labeling: Better precision powered by retraining on the New dataset.

Training Methodology

FAL-1.5 was trained using a combination of supervised learning on a diverse video dataset and reinforcement learning from human feedback (RLHF). This dual approach ensures the model not only learns from labeled data but also adapts to user preferences and real-world scenarios.