| --- |
| 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 |
| ``` |
|
|
