README.md

metadata

license: mit
pipeline_tag: image-classification
tags:
  - onnx
  - onnxruntime
  - image-classification
  - binary-classification
library_name: onnx

cstria0106/shit-detector

ONNX image classifier for detecting whether an image contains feces or toilet waste/staining under this project's labeling policy.

Try It In Your Browser

Live browser demo: https://huggingface.co/spaces/cstria0106/shit-detector-browser

Runs fully client-side: the demo downloads this ONNX model in the browser and runs inference locally with ONNX Runtime Web.

Files

• shit_detector.onnx: ONNX model exported with dynamic batch axis.
• metadata.json: preprocessing values, threshold, class names, evaluation metrics, and deployment notes.
• inference.py: minimal ONNX Runtime inference helper.
• requirements.txt: runtime dependencies for the helper.

Labels

Class order: ['shit', 'not_shit'].

The positive class is shit. The default decision threshold is 0.149 on the positive-class probability.

Dataset

The model was trained on a project-specific binary image dataset.

Split	shit	not_shit
Train	483	2,894
Validation	120	686
Test	189	1,003

Training

• Objective: 2-class cross entropy
• Optimizer: AdamW
• Learning rate: 1.5e-4
• Weight decay: 0.01
• Epochs: up to 80
• Best checkpoint selected by validation F1
• Backbone frozen for first 5 epochs, then unfrozen
• Balanced sampling enabled with positive sampling fraction 0.40
• Augmentation: random resized crop, horizontal flip, color jitter
• Positive-only color augmentation was used to improve robustness to color-shifted positive examples.

Model Specs

• Base model: tf_efficientnet_b0.ns_jft_in1k
• Format: ONNX, opset 17
• Input size: 256 x 256 RGB image
• Parameters: 4010110
• ONNX size: 15.34 MB
• Class order: ['shit', 'not_shit']
• Positive threshold: 0.149

Evaluation Snapshot

• Precision: 93.51%
• Recall: 91.53%
• F1: 92.51%
• ROC-AUC: 99.09%
• PR-AUC: 97.42%

These metrics come from the local evaluation set recorded in metadata.json; they are not a public benchmark.

Intended Use

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.

Runtime

pip install -r requirements.txt
python inference.py path/to/image.jpg

Deployment Target

• Target: lattepanda_n4120
• CPU: Intel Celeron N4120
• Model parameters: 4010110
• ONNX size MB: 15.341004371643066