visionkit-pro 0.1.2

Reusable training, evaluation, and Grad-CAM utilities for CSV-backed image classification.

visionkit

visionkit is a small PyTorch package for image-classification projects where image metadata is stored in a CSV file and the image files live in a directory. It provides a reusable workflow for training, evaluation, prediction tables, and Grad-CAM visualization.

It turns the notebook workflow into a package that can be reused with different CSV files and image folders:

• Load image filenames, labels, and train/validation splits from a CSV
• Train torchvision models such as EfficientNet or ResNet
• Save checkpoints, training logs, prediction CSVs, and metric CSVs
• Generate Grad-CAM heatmaps and overlay images

Quick Start

Prepare an image directory and a CSV file.

your_project/
  data/
    images/
      case001.png
      case002.png
      case003.png
    meta.csv
  outputs/

The minimal meta.csv should look like this:

filename,label,split
case001.png,F,train
case002.png,N,train
case003.png,F,validation
case004.png,N,validation

Train a model:

visionkit train \
  --csv data/meta.csv \
  --image-dir data/images \
  --output-dir outputs/experiment_001 \
  --classes F N \
  --positive-class N \
  --epochs 30

Evaluate the validation split:

visionkit evaluate \
  --csv data/meta.csv \
  --image-dir data/images \
  --output-dir outputs/experiment_001 \
  --checkpoint outputs/experiment_001/models/best.pt \
  --classes F N \
  --positive-class N \
  --split validation

Generate Grad-CAM overlays:

visionkit gradcam \
  --csv data/meta.csv \
  --image-dir data/images \
  --output-dir outputs/experiment_001 \
  --checkpoint outputs/experiment_001/models/best.pt \
  --classes F N \
  --positive-class N \
  --split validation \
  --target-class N

Required CSV

For CLI training, the CSV usually needs these three columns.

column	required	example	meaning
filename	yes	case001.png	Image path relative to --image-dir
label	yes	F / N	Class label
split	yes	train / validation	Which split the row belongs to

The default column names are filename, label, and split. If your CSV uses different names, pass them explicitly:

visionkit train \
  --csv data/metadata.csv \
  --image-dir data/fig \
  --output-dir outputs/run_001 \
  --filename-column image_name \
  --label-column diagnosis \
  --split-column fixed_family_level_split \
  --train-split train \
  --val-split validation

Extra CSV columns are allowed. Columns such as patient_id, family_id, or age are carried through into the prediction CSVs.

filename,label,split,patient_id,family_id
case001.png,F,train,P001,A
case002.png,N,train,P002,B
case003.png,F,validation,P003,C

Image Paths

filename is interpreted as a path relative to --image-dir.

If all images are in the same folder:

filename,label,split
case001.png,F,train
case002.png,N,validation

If images are stored in subfolders, include the subfolder in filename:

filename,label,split
train/case001.png,F,train
validation/case002.png,N,validation

This expects files like:

data/images/train/case001.png
data/images/validation/case002.png

Classes And Positive Class

For binary classification, the order of --classes defines the class indices.

--classes F N

This means:

• F is class 0
• N is class 1

If you want AUC, sensitivity, specificity, and PR-AUC to use a specific positive class, set it explicitly:

--positive-class N

If --classes is omitted, class names are inferred from the labels in the CSV. For reproducible research or paper figures, it is better to pass --classes explicitly because class order affects the numeric labels and positive class.

Outputs

With --output-dir outputs/experiment_001, the package creates files like:

outputs/experiment_001/
  models/
    best.pt
    epoch_001.pt
    epoch_002.pt
  logs/
    training_history.csv
  predictions/
    validation_predictions.csv
    validation_predictions_metrics.csv
    gradcam_validation.csv
    gradcam_validation_metrics.csv
  gradcam/
    validation/
      case001__pred-N.png

By default, best.pt is the checkpoint from the epoch with the best val_auc.

Common Commands

Use a torchvision model other than EfficientNet-B3:

visionkit train \
  --csv data/meta.csv \
  --image-dir data/images \
  --output-dir outputs/resnet50_run \
  --architecture resnet50 \
  --image-size 224x224 \
  --classes F N \
  --positive-class N

Use a CSV layout similar to the original notebook:

visionkit train \
  --csv train_val/data/meta/fixed/fixed_family_level_split.csv \
  --image-dir train_val/data/fig \
  --output-dir train_val/review_outputs/family_level_split_efficientnetb3_seed123 \
  --filename-column filename \
  --label-column label \
  --split-column fixed_family_level_split \
  --train-split train \
  --val-split validation \
  --classes F N \
  --positive-class N \
  --architecture efficientnet_b3 \
  --image-size 300x300

Python API

from pathlib import Path

from visionkit.config import ExperimentConfig
from visionkit.pipeline import run_training, run_evaluation, run_gradcam

config = ExperimentConfig(
    csv_path=Path("data/meta.csv"),
    image_dir=Path("data/images"),
    output_dir=Path("outputs/experiment_001"),
    class_names=["F", "N"],
    positive_class="N",
    split_column="split",
    train_split="train",
    val_split="validation",
    architecture="efficientnet_b3",
    image_size=(300, 300),
    epochs=30,
)

model, history = run_training(config)

predictions, metrics = run_evaluation(
    config,
    checkpoint_path=config.output_dir / "models" / "best.pt",
    split_name="validation",
)

gradcam_results, gradcam_metrics = run_gradcam(
    config,
    checkpoint_path=config.output_dir / "models" / "best.pt",
    split_name="validation",
    target_class="N",
)

Install

Use the package locally in editable mode:

pip install -e .

If PyTorch is not installed yet, install torch and torchvision first using the command appropriate for your CPU or CUDA environment.