exam-behavior-classifier

Exam Behavior Classifier — R(2+1)D-18

Classifies a short clip of a single student into one of five exam behaviors: normal, copying, gesture, mobile, notes.

• Architecture: torchvision r2plus1d_18, Kinetics-400 pretrained, 5-way head.
• Input: 16 uniformly-sampled frames, center-cropped to 112×112, Kinetics normalization.
• Training data: 251 clips from the Kaggle ExamCheating_MultiV dataset (V1 prefix-labeled
  • 30 hand-labeled V2 clips).
• Test (n=38, leakage-free group split): accuracy 71.1%, macro-F1 0.680. On the original clean distribution: 78.8%; on out-of-distribution phone footage: 20% — see the repo for the full distribution-gap analysis.

⚠️ Research & education only — not a proctoring system. Outputs are suggestions for a human to review, never evidence of cheating. Full limitations and ethics discussion in the model card.

Code, training pipeline, and demo app: github.com/MunkhbayarA/exam-cheating-detection

Usage

import torch, torch.nn as nn
from torchvision.models.video import r2plus1d_18
from huggingface_hub import hf_hub_download

CLASSES = ["normal", "copying", "gesture", "mobile", "notes"]

ckpt_path = hf_hub_download("mbradiant/exam-behavior-classifier", "best_model.pt")
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True)
model = r2plus1d_18()
model.fc = nn.Linear(model.fc.in_features, len(CLASSES))
model.load_state_dict(ckpt["model"])
model.eval()
# input: float tensor (B, 3, 16, 112, 112), Kinetics-normalized