| ---
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| license: mit
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| pipeline_tag: image-classification
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| tags:
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| - onnx
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| - onnxruntime
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| - image-classification
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| - binary-classification
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| library_name: onnx
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| ---
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|
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| # cstria0106/shit-detector
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|
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| ONNX image classifier for detecting whether an image contains feces or toilet waste/staining under this project's labeling policy.
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| ## Try It In Your Browser
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| **Live browser demo:** [https://huggingface.co/spaces/cstria0106/shit-detector-browser](https://huggingface.co/spaces/cstria0106/shit-detector-browser)
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| Runs fully client-side: the demo downloads this ONNX model in the browser and runs inference locally with ONNX Runtime Web.
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|
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| ## Files
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| - `shit_detector.onnx`: ONNX model exported with dynamic batch axis.
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| - `metadata.json`: preprocessing values, threshold, class names, evaluation metrics, and deployment notes.
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| - `inference.py`: minimal ONNX Runtime inference helper.
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| - `requirements.txt`: runtime dependencies for the helper.
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|
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| ## Labels
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| Class order: `['shit', 'not_shit']`.
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| The positive class is `shit`. The default decision threshold is `0.149` on the positive-class probability.
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|
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| ## Dataset
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| The model was trained on a project-specific binary image dataset.
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| | Split | shit | not_shit |
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| |---|---:|---:|
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| | Train | 483 | 2,894 |
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| | Validation | 120 | 686 |
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| | Test | 189 | 1,003 |
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|
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| ## Training
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|
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| - Objective: 2-class cross entropy
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| - Optimizer: AdamW
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| - Learning rate: `1.5e-4`
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| - Weight decay: `0.01`
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| - Epochs: up to 80
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| - Best checkpoint selected by validation F1
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| - Backbone frozen for first 5 epochs, then unfrozen
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| - Balanced sampling enabled with positive sampling fraction `0.40`
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| - Augmentation: random resized crop, horizontal flip, color jitter
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| - Positive-only color augmentation was used to improve robustness to color-shifted positive examples.
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|
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| ## Model Specs
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| - Base model: `tf_efficientnet_b0.ns_jft_in1k`
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| - Format: ONNX, opset `17`
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| - Input size: `256 x 256` RGB image
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| - Parameters: `4010110`
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| - ONNX size: `15.34 MB`
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| - Class order: `['shit', 'not_shit']`
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| - Positive threshold: `0.149`
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|
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| ## Evaluation Snapshot
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| - Precision: 93.51%
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| - Recall: 91.53%
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| - F1: 92.51%
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| - ROC-AUC: 99.09%
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| - PR-AUC: 97.42%
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| These metrics come from the local evaluation set recorded in `metadata.json`; they are not a public benchmark.
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|
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| ## Intended Use
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| This model is intended for project-specific binary image triage. It may produce false positives on visually similar brown food, stains, mud, and other hard negatives, and false negatives on small, occluded, color-shifted, or unusual positive cases.
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|
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| ## Runtime
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| ```bash
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| pip install -r requirements.txt
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| python inference.py path/to/image.jpg
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| ```
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|
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| ## Deployment Target
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| - Target: `lattepanda_n4120`
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| - CPU: `Intel Celeron N4120`
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| - Model parameters: `4010110`
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| - ONNX size MB: `15.341004371643066`
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|
