How dranzerd/inference-time-image-compression- works
This repository is a research codebase comparing image-compression variants and neural-network approaches for binary chest-X-ray classification (effusion versus healthy). The intended workflow is to prepare image/split data, train a classifier, evaluate predictions, and retain metrics; cross-validation repeats training and evaluation across folds. The tree also contains a WebP compression utility and stored reports. The README is only a title, and several model/dataset implementations were not sampled, so links for those branches are limited to what their paths support.
Main entry points: dataset.py MLP dataset · train.py MLP training · evaluate.py MLP evaluation · mlp.py MLP classifier
Components 22
Data preparation
- WebP compression
MLP/compression/compress_webP.py - MLP split data
MLP/data - DeiT fold data
DeiT - ResNet fold data
ResNet18
MLP workflow
- MLP dataset
MLP/dataset/dataset.py - MLP classifier
MLP/models/mlp.py - MLP training
MLP/train.py - MLP cross-validation
MLP/cross_validation/run_5fold.py
Vision workflows
- DeiT training
DeiT/train_latest.py - DeiT model
DeiT/model.py - DeiT fold runner
DeiT/run_kfold.py - ResNet training
ResNet18/train_latest.py - ResNet model
ResNet18/model.py - ResNet fold runner
ResNet18/run_kfold.py
Evaluation and results
- MLP evaluation
MLP/evaluate.py - MLP fold evaluation
MLP/cross_validation/evaluate_fold.py - DeiT evaluation
DeiT/eval_latest.py - ResNet evaluation
ResNet18/eval_latest.py - DeiT reports
DeiT/reports_googlephotos_deit_s - ResNet reports
ResNet18/reports_googlephotos - MLP results
MLP/results
Other components
- Researcher
Connections 30
- Researcher → WebP compression: runs
- Researcher → MLP training: runs
- Researcher → MLP cross-validation: runs
- Researcher → DeiT fold runner: runs
- Researcher → ResNet fold runner: runs
- Researcher → MLP evaluation: runs
- WebP compression → MLP split data: reads split references
- MLP training → MLP dataset: creates datasets
- MLP training → MLP classifier: trains
- MLP training → MLP results: writes results
- MLP dataset → MLP split data: reads CSV
- MLP dataset → Researcher: reads local images
- MLP cross-validation → MLP dataset: uses
- MLP cross-validation → MLP classifier: trains folds
- MLP fold evaluation → MLP dataset: loads fold data
- MLP fold evaluation → MLP classifier: evaluates
- MLP fold evaluation → MLP results: writes metrics
- MLP evaluation → MLP dataset: loads test data
- MLP evaluation → MLP classifier: evaluates
- MLP evaluation → MLP results: writes metrics
- DeiT fold runner → DeiT training: dispatches folds
- DeiT fold runner → DeiT evaluation: dispatches evaluation
- DeiT training → DeiT fold data: uses fold data
- DeiT evaluation → DeiT fold data: uses fold data
- DeiT evaluation → DeiT reports: writes reports
- ResNet fold runner → ResNet training: dispatches folds
- ResNet fold runner → ResNet evaluation: dispatches evaluation
- ResNet training → ResNet fold data: uses fold data
- ResNet evaluation → ResNet fold data: uses fold data
- ResNet evaluation → ResNet reports: writes reports