This is the Linux app named SimSiam whose latest release can be downloaded as simsiamsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named SimSiam with OnWorks for free.
Volg deze instructies om deze app uit te voeren:
- 1. Download deze applicatie op uw pc.
- 2. Voer in onze bestandsbeheerder https://www.onworks.net/myfiles.php?username=XXXXX in met de gebruikersnaam die u wilt.
- 3. Upload deze applicatie in zo'n bestandsbeheerder.
- 4. Start de OnWorks Linux online of Windows online emulator of MACOS online emulator vanaf deze website.
- 5. Ga vanuit het OnWorks Linux-besturingssysteem dat u zojuist hebt gestart naar onze bestandsbeheerder https://www.onworks.net/myfiles.php?username=XXXXX met de gewenste gebruikersnaam.
- 6. Download de applicatie, installeer hem en voer hem uit.
SCREENSHOTS
Ad
Sim Siam
PRODUCTBESCHRIJVING
SimSiam is a PyTorch implementation of “Exploring Simple Siamese Representation Learning” by Xinlei Chen and Kaiming He. The project introduces a minimalist approach to self-supervised learning that avoids negative pairs, momentum encoders, or large memory banks—key complexities of prior contrastive methods. SimSiam learns image representations by maximizing similarity between two augmented views of the same image through a Siamese neural network with a stop-gradient operation, preventing feature collapse. This elegant yet effective design achieves strong results in unsupervised learning benchmarks such as ImageNet without requiring contrastive losses. The repository provides scripts for both unsupervised pre-training and linear evaluation, using a ResNet-50 backbone by default. It is compatible with multi-GPU distributed training and can be fine-tuned or transferred to downstream tasks like object detection following the same setup as MoCo.
Kenmerken
- Minimal self-supervised learning framework without negative pairs or momentum encoders
- PyTorch-based implementation optimized for distributed multi-GPU training
- Fully reproducible training pipeline for ImageNet using default hyperparameters from the paper
- Includes both unsupervised pre-training and linear evaluation scripts
- LARS optimizer support via NVIDIA Apex for large-batch training
- Compatible with object detection transfer setups from MoCo
Programmeertaal
Python
Categorieën
This is an application that can also be fetched from https://sourceforge.net/projects/simsiam.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
