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lightly-train

lightly-ai/lightly-train

All-in-one training for vision models: pretraining, fine-tuning, distillation.

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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1.7k stars116 forksLast push Sep 14, 2026 Python AGPL-3.0

Decision brief

Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.

Requirements:
Min 8 GB RAM

Observed Jul 12, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Very active (5d since push)
As of Sep 20, 2026
Provenance
Not a fork · Organization account
As of Sep 20, 2026
Security (OSV)
No lockfile
As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install lightly-train
PyPI

Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A Python-based repository providing comprehensive tools and methods for the training of various computer vision models including YOLO, ViTs, RT-DETR, and DINOv3 with support for pretraining, fine-tuning, and knowledge distillation techniques.

Capability facts

CLI
CLI entrypoint

Source: pyproject.toml:[project.scripts] · Sep 20, 2026

Languages
python

Source: github.language+pyproject.toml · Sep 20, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 20, 2026)

Install Lightly**Train** on Python 3.8+ for Windows, Linux or MacOS with:
Source link

Tags

README

Installation Install Lightly Train on Python 3.8+ for Windows, Linux or MacOS with: License Lightly Train offers flexible licensing options to suit your specific needs: AGPL 3.0 License : Perfect for open source projects, academic research, and community contributions. Share your...

For agents

This page has a .md twin and JSON over the API.

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