PneumoOps — ChestMNIST MobileNetV3-small
This repository contains two model versions for the PneumoOps MLOps pipeline:
- Model A (Baseline):
mobilenetv3_chestmnist.pth— Standard PyTorch checkpoint - Model B (Optimized):
mobilenetv3_chestmnist.onnx— ONNX-exported for faster inference
Both models are identical in architecture (MobileNetV3-small) and weights. The ONNX version is used for inference time optimization in A/B testing.
Dataset
ChestMNIST — 14-class multi-label chest X-ray classification
78,468 training images, 224×224 pixels, grayscale (converted to 3-channel).
Classes (14)
Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, Hernia
Performance (Test Set)
| Metric | Score |
|---|---|
| Macro AUROC | 0.808 |
| Macro AUPRC | 0.210 |
| Micro F1 | 0.343 |
Usage in PneumoOps
These artifacts are loaded by the FastAPI backend and selected via a weighted A/B router:
- 60% of requests → PyTorch model
- 40% of requests → ONNX model
The backend also computes a drift score using baseline_stats.json to detect
out-of-distribution inputs in real time.