like9426334946/selfmodel

How like9426334946/selfmodel works

ModelTrial is a configurable medical-image segmentation training and evaluation project. The main workflow loads configuration, builds a dataset and model, trains with a selected loss, evaluates validation metrics, and saves model and metric artifacts. It supports Kvasir-SEG and ACDC datasets and seven model choices; a separate visualization script logs validation images and training metrics to TensorBoard. The test entry point is documented but its implementation was not sampled, so its internal flow is not inferred.

Main entry points: models/__init__.py Model registry · train.py Training runner · visualize_train.py TensorBoard visualizer · datasets/__init__.py Dataset builder

Components 21

Training workflow

Run configuration utils/config.py
Training runner train.py
Test entry test.py
Training artifacts

Segmentation data

Dataset builder datasets/__init__.py
Kvasir dataset datasets/kvasir_dataset.py
ACDC dataset datasets/acdc_dataset.py

Model choices

Model registry models/__init__.py
Model 01 models/model_01.py
Model 02 models/model_02.py
Model 03 models/model_03.py
Model 04 models/model_04.py
Model 05 models/model_05.py
Model 06 models/model_06.py
Model 07 models/model_07.py

Evaluation and visualization

Loss and predictions utils/task.py
Validation metrics utils/metrics.py
Training curves utils/plot.py
TensorBoard visualizer visualize_train.py
TensorBoard logs

Other components

Researcher

Connections 26

Researcher → Run configuration: sets options README.md
Researcher → Training runner: starts training README.md
Researcher → Test entry: starts testing README.md
Training runner → Run configuration: loads settings train.py
Training runner → Dataset builder: builds splits train.py
Training runner → Model registry: builds model train.py
Training runner → Loss and predictions: builds loss train.py
Training runner → Validation metrics: evaluates model train.py
Training runner → Training curves: plots history train.py
Training runner → Training artifacts: writes checkpoints and metrics train.py
Dataset builder → Kvasir dataset: constructs dataset datasets/__init__.py
Dataset builder → ACDC dataset: constructs dataset datasets/__init__.py
Model registry → Model 01: selects model models/__init__.py
Model registry → Model 02: selects model models/__init__.py
Model registry → Model 03: selects model models/__init__.py
Model registry → Model 04: selects model models/__init__.py
Model registry → Model 05: selects model models/__init__.py
Model registry → Model 06: selects model models/__init__.py
Model registry → Model 07: selects model models/__init__.py
Validation metrics → Loss and predictions: predicts classes utils/metrics.py
TensorBoard visualizer → Run configuration: loads settings visualize_train.py
TensorBoard visualizer → Dataset builder: builds splits visualize_train.py
TensorBoard visualizer → Model registry: builds model visualize_train.py
TensorBoard visualizer → Loss and predictions: builds loss and predictions visualize_train.py
TensorBoard visualizer → Validation metrics: evaluates model visualize_train.py
TensorBoard visualizer → TensorBoard logs: writes summaries visualize_train.py

More Python diagrams: latent-spaces/brag · semantica-agi/semantica · originalankur/maptoposter · redis/redis-py · k2-fsa/omnivoice

Drawn by GitDiagram on September 30, 2026 from the repository's file tree, README and source files. Read as Markdown ·