This is the Linux app named DomainBed whose latest release can be downloaded as DomainBedsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named DomainBed with OnWorks for free.
Ikuti petunjuk ini untuk menjalankan aplikasi ini:
- 1. Download aplikasi ini di PC Anda.
- 2. Masuk ke file manager kami https://www.onworks.net/myfiles.php?username=XXXXX dengan username yang anda inginkan.
- 3. Upload aplikasi ini di filemanager tersebut.
- 4. Jalankan emulator online OnWorks Linux atau Windows online atau emulator online MACOS dari situs web ini.
- 5. Dari OS Linux OnWorks yang baru saja Anda mulai, buka file manager kami https://www.onworks.net/myfiles.php?username=XXXXX dengan nama pengguna yang Anda inginkan.
- 6. Download aplikasinya, install dan jalankan.
SCREENSHOT:
DomainBed
DESKRIPSI:
DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and Invariant Risk Minimization (IRM) to more advanced techniques like Domain Adversarial Neural Networks (DANN), Adaptive Risk Minimization (ARM), and Invariance Principle Meets Information Bottleneck (IB-ERM/IB-IRM). DomainBed also integrates multiple standard datasets—including RotatedMNIST, PACS, VLCS, Office-Home, DomainNet, and subsets from WILDS—allowing consistent experimentation across image classification tasks.
Fitur
- Comprehensive PyTorch suite for domain generalization research and benchmarking
- Implements 25+ algorithms including ERM, IRM, DANN, Fish, and more
- Includes diverse domain generalization datasets such as PACS, DomainNet, and WILDS subsets
- Supports reproducible model selection methods and evaluation protocols
- Automates large-scale training sweeps and hyperparameter optimization
- Provides detailed result collection and LaTeX-compatible reporting utilities
Bahasa Pemrograman
Ular sanca
KATEGORI
This is an application that can also be fetched from https://sourceforge.net/projects/domainbed.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.