Frame Prediction Averaging

Overview

Video classification models often process individual frames independently. Averaging the sigmoid outputs across all sampled frames reduces noise from individual frame variability (occlusion, motion blur, detection failures) and produces a more stable video-level prediction. This is simpler and often competitive with temporal models like LSTMs for binary classification tasks.

Quick Start

import torch
import numpy as np

def predict_video(model, frames, device="cuda"):
    model.eval()
    batch = torch.stack(frames).to(device)
    with torch.no_grad():
        logits = model(batch).squeeze()
        probs = torch.sigmoid(logits)
    return probs.mean().item()

# frames: list of N preprocessed tensors from sampled video frames
video_prob = predict_video(model, face_tensors)

Workflow

1. Sample N frames from the video (typically 15-20)
2. Extract and preprocess face crops from each frame
3. Run each frame through the classifier to get sigmoid probabilities
4. Average all per-frame probabilities for the final video-level score
5. Optionally clip to [0.05, 0.95] to avoid extreme predictions from single-frame errors

Key Decisions

• Averaging vs. max: mean is robust to outliers; max captures the single most suspicious frame (higher recall, lower precision)
• Weighted average: weight frames by face detection confidence to downweight low-quality detections
• Frames with no face: exclude from average rather than using a default value
• Clipping: clip final probability to [0.01, 0.99] to avoid log-loss penalties from extreme values
• vs. temporal models: averaging ignores frame order but is more robust with limited training data

References

• Inference Demo