This is the Linux app named maskrcnn-benchmark whose latest release can be downloaded as Initialreleasesourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named maskrcnn-benchmark with OnWorks for free.
Urmați aceste instrucțiuni pentru a rula această aplicație:
- 1. Ați descărcat această aplicație pe computer.
- 2. Introduceți în managerul nostru de fișiere https://www.onworks.net/myfiles.php?username=XXXXX cu numele de utilizator pe care îl doriți.
- 3. Încărcați această aplicație într-un astfel de manager de fișiere.
- 4. Porniți emulatorul online OnWorks Linux sau Windows online sau emulatorul online MACOS de pe acest site web.
- 5. Din sistemul de operare OnWorks Linux pe care tocmai l-ați pornit, accesați managerul nostru de fișiere https://www.onworks.net/myfiles.php?username=XXXXX cu numele de utilizator dorit.
- 6. Descărcați aplicația, instalați-o și rulați-o.
SCREENSHOTS
Ad
maskrcnn-benchmark
DESCRIERE
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.
Categorii
- 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
Limbaj de programare
Piton
Categorii
This is an application that can also be fetched from https://sourceforge.net/projects/maskrcnn-benchmark.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
