ml-cvnets

Why we included this project

Training a new vision model usually means gluing together dataloaders, configs, and training loops by hand. CVNets is one of the few toolkits that handles that plumbing for you, wrapping PyTorch to cover classification, object detection, semantic segmentation, and CLIP-style foundation models from a single configuration-driven codebase. You can switch between MobileNet, EfficientNet, Swin, ViT, or Mask R-CNN without maintaining separate training pipelines, and the repo ships ready-to-run configs and examples for standard benchmarks like ImageNet, MS-COCO, and ADE20K, so reproducing a published baseline takes far less fiddling. The project comes out of Apple's research group and includes utilities for converting trained PyTorch models to CoreML, handy if you plan to deploy on Apple platforms.