This repository provides a simple example of performing inference using the MobileNetV3 model with the Tract library. The model is evaluated using the mobilenetv3_model.onnx file on a test dataset, with performance metrics calculated, including true positives, false positives, true negatives, false negatives, and overall accuracy. The results are compared against a Python implementation.
The dataset used for fine-tuning the model is sourced from Kaggle. This dataset can be organized into three subsets:
- Training Set: 70% of the total dataset
- Validation Set: 20% of the total dataset
- Test Set: 10% of the total dataset
Note that the test dataset is used exclusively for evaluation. and it can be found here.
The results are listed as follows:
- True Positives (Violence): 491
- True Negatives (Non-Violence): 404
- False Positives (Predicted Violence, Actual Non-Violence): 120
- False Negatives (Predicted Non-Violence, Actual Violence): 94
- Total Images: 1109
- Accuracy: 80.70%