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.
Segui queste istruzioni per eseguire questa app:
- 1. Scaricata questa applicazione sul tuo PC.
- 2. Entra nel nostro file manager https://www.onworks.net/myfiles.php?username=XXXXX con il nome utente che desideri.
- 3. Carica questa applicazione in tale file manager.
- 4. Avviare l'emulatore online OnWorks Linux o Windows online o l'emulatore online MACOS da questo sito Web.
- 5. Dal sistema operativo OnWorks Linux che hai appena avviato, vai al nostro file manager https://www.onworks.net/myfiles.php?username=XXXXX con il nome utente che desideri.
- 6. Scarica l'applicazione, installala ed eseguila.
IMMAGINI
Ad
SimSiam
DESCRIZIONE
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.
Caratteristiche
- 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
Linguaggio di programmazione
Python
Categorie
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.
