**End-to-end computer vision framework for image classification, object detection, and semantic segmentation — with real mAP/IoU math, PyTorch training loops, data augmentation, and model export.**
Browse the actual product documentation and code examples included in this toolkit.
Key features of Computer Vision Toolkit
• **Image classification** — build classification pipelines with pre-trained ResNet/EfficientNet/ViT backbones, custom heads, and label management • **Object detection** — anchor-based detection with configurable anchor scales, multi-scale feature maps, and NMS post-processing • **Semantic segmentation** — UNet and FPN decoder architectures on any backbone, with per-pixel loss computation and boundary refinement • **Data augmentation library** — composable transforms (flip, rotate, crop, color jitter, blur, grayscale) with `Compose`, `OneOf`, and per-transform probability control • **Dataset loaders** — `ImageFolderDataset` for classification, `DetectionDataset` for COCO/VOC/YOLO-format annotations, with lazy loading and caching • **Training loops** — full PyTorch training with mixed precision (AMP), gradient accumulation, cosine/step/plateau LR scheduling, warmup, and automatic checkpointing
**Image classification** — build classification pipelines with pre-trained ResNet/EfficientNet/ViT backbones, custom heads, and label management
**Object detection** — anchor-based detection with configurable anchor scales, multi-scale feature maps, and NMS post-processing
**Semantic segmentation** — UNet and FPN decoder architectures on any backbone, with per-pixel loss computation and boundary refinement
**Data augmentation library** — composable transforms (flip, rotate, crop, color jitter, blur, grayscale) with `Compose`, `OneOf`, and per-transform probability control
**Dataset loaders** — `ImageFolderDataset` for classification, `DetectionDataset` for COCO/VOC/YOLO-format annotations, with lazy loading and caching
**Training loops** — full PyTorch training with mixed precision (AMP), gradient accumulation, cosine/step/plateau LR scheduling, warmup, and automatic checkpointing
Configure Computer Vision Toolkit parameters to see how the product works.
pip install -r requirements.txt