Exam Behavior Classifier

--- license: mit pipeline_tag: video-classification tags: - video - action-recognition - r2plus1d - pytorch library_name: pytorch --- # 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](https://github.com/MunkhbayarA/exam-cheating-detection/blob/main/model_card.md). **Code, training pipeline, and demo app:** [github.com/MunkhbayarA/exam-cheating-detection](https://github.com/MunkhbayarA/exam-cheating-detection) ## Usage ```python 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 ```

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