--- title: "lightly vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightly-ai-lightly-vs-nvidia-deeplearningexamples" tools: ["lightly-ai-lightly", "nvidia-deeplearningexamples"] --- # lightly vs DeepLearningExamples *GraphCanon updated Sep 20, 2026* ## Verdict Pick lightly if lightly specializes in self-supervised learning for image data to improve computer vision models without labeled datasets; pick DeepLearningExamples if deepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N. [lightly](https://docs.lightly.ai/self-supervised-learning/) reports 3.8k GitHub stars, 367 forks, and 107 open issues, last pushed Sep 18, 2026. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) has 15k stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. Figures are from public GitHub metadata via [lightly's repository](https://github.com/lightly-ai/lightly) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [lightly](/tools/lightly-ai-lightly.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | A python library for self-supervised learning on images. | State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure | | Stars | 3,812 | 14,847 | | Forks | 367 | 3,406 | | Open issues | 107 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | Lightly specializes in self-supervised learning for image data to improve computer vision models without labeled datasets. | DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N | | Persona | - | - | | Runtime | - | - | | License | MIT | The license information for DeepLearningExamples is unknown. | | Categories | Computer Vision, Model Training | Computer Vision, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lightly](/tools/lightly-ai-lightly.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 766d | | Open issues (now) | 107 | 321 | | Stars delta | +28 (30d) | +3 (30d) | | Open issues delta | +14 (30d) | 0 (30d) | | Full report | [trust report](/tools/lightly-ai-lightly/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: lightly - **Adopt for:** Lightly specializes in self-supervised learning for image data to improve computer vision models without labeled datasets. ## Decision facts: DeepLearningExamples - **Requirements:** Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS. - **Adopt for:** DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N - **License detail:** The license information for DeepLearningExamples is unknown. ## Choose when ### Choose lightly if… - lightly is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to lightly: contrastive-learning, embeddings, self-supervised-learning. - You need to enhance model performance with unlabeled image data. ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; lightly is Python. - Requirements: Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS.. - Tags unique to DeepLearningExamples: drug-discovery, forecasting, large-language-models, mxnet. - Also covers Inference & Serving. - When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition. ## When NOT to use lightly - Labeled datasets are abundant and of high quality for your use case. - Project requirements strictly limit the use of Python-based libraries. ## When NOT to use DeepLearningExamples - If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware. - If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments. - When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks. ## Common questions ### What is the difference between lightly and DeepLearningExamples? lightly: A python library for self-supervised learning on images.. DeepLearningExamples: State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure. See the comparison table for live GitHub stats and shared categories. ### When should I choose lightly over DeepLearningExamples? Choose lightly over DeepLearningExamples when lightly is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to lightly: contrastive-learning, embeddings, self-supervised-learning; You need to enhance model performance with unlabeled image data. ### When should I choose DeepLearningExamples over lightly? Choose DeepLearningExamples over lightly when DeepLearningExamples is primarily Jupyter Notebook; lightly is Python; Requirements: Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS.; Tags unique to DeepLearningExamples: drug-discovery, forecasting, large-language-models, mxnet; Also covers Inference & Serving; When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition. ### When should I avoid lightly? Labeled datasets are abundant and of high quality for your use case. Project requirements strictly limit the use of Python-based libraries. ### When should I avoid DeepLearningExamples? If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware. If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments. When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks. ### Is lightly or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,847 vs 3,812). Stars measure visibility, not whether either tool fits your constraints. ### Are lightly and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to lightly or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [lightly alternatives](/tools/lightly-ai-lightly/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([lightly markdown twin](/tools/lightly-ai-lightly/alternatives.md), [DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/lightly-ai-lightly-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lightly or DeepLearningExamples? lightly: Very active. DeepLearningExamples: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for lightly and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lightly trust report](/tools/lightly-ai-lightly/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lightly-ai-lightly`](/api/graphcanon/graph?tool=lightly-ai-lightly) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_