maskrcnn benchmark

We have hosted the application maskrcnn benchmark in order to run this application in our online workstations with Wine or directly.

Quick description about maskrcnn benchmark:

Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. The framework integrates critical components�region proposal networks (RPNs), RoIAlign layers, mask heads, and backbone architectures such as ResNet and FPN�optimized for both accuracy and speed. It supports multi-GPU distributed training, mixed precision, and custom data loaders for new datasets. Built as a reference implementation, it became a foundation for the next-generation Detectron2, yet remains widely used for research needing a stable, reproducible environment. Visualization tools, model zoo checkpoints, and benchmark scripts make it easy to replicate state-of-the-art results or fine-tune models for custom tasks.

Features:

• High-performance implementations of Mask R-CNN, Faster R-CNN, and keypoint models
• Modular components for RPNs, RoIAlign, mask heads, and backbones
• Multi-GPU distributed training and mixed precision support
• Dataset support and loaders for COCO, Pascal VOC, and custom datasets
• Visualization and evaluation tools for detection and segmentation results
• Reproducible reference implementation for benchmarking and fine-tuning

Programming Language: Python.
Categories: