[P] cv-pipeline: A minimal PyTorch toolkit for CV researchers who hate boilerplate

To all DS and ML researchers

If someone got tired of copy-pasting the same data loading, training loops, and export code for every CV project. So I built a toolkit that handles the boring stuff.

What it does:

from cv_pipeline import quick_train, analyze_dataset, export_model # Analyze your dataset analyze_dataset("./my_images") # Train (one line) model, history = quick_train("./my_images", model="efficientnet_b0", epochs=10) # Export for deployment export_model(model, "model.onnx", format="onnx") 

Key features:

• Data loading – Point to a folder, get DataLoaders. Handles splits, augmentation, and normalisation.
• 50+ architectures – ResNet, EfficientNet, ViT, MobileNet via timm. One-line model loading.
• Dataset analysis – Class distribution, imbalance detection, image stats.
• Model comparison: benchmark multiple architectures on your data.
• Export – TorchScript, ONNX, state_dict.
• CLI – cv-pipeline train --data ./images --model resnet50 --epochs 20
• Notebook generator – Auto-generate starter notebooks for classification/detection/segmentation.

CLI example:

# Analyze dataset cv-pipeline analyze --data ./images # Train cv-pipeline train --data ./images --model efficientnet_b0 --epochs 20 # Compare models cv-pipeline compare --models resnet50,efficientnet_b0,vit_base --data ./images 

Not a framework – just utilities. Use with your existing PyTorch code. No lock-in.

Built for rapid prototyping and experiment iteration. Includes configs for medical imaging, manufacturing QC, retail, and document processing use cases.

GitHub: https://github.com/var1914/pytorch-ml-pipeline

Feedback welcome. What utilities would you add?