--- title: "caffe vs Video-LLaMA" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bvlc-caffe-vs-damo-nlp-sg-video-llama" tools: ["bvlc-caffe", "damo-nlp-sg-video-llama"] --- # caffe vs Video-LLaMA *GraphCanon updated Aug 18, 2026* ## Verdict Pick caffe if caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency; pick Video-LLaMA if video-LLaMA is an audio-visual language model that enhances video and audio understanding capabilities for language models. [caffe](http://caffe.berkeleyvision.org/) reports 35k GitHub stars, 18k forks, and 1.5k open issues, last pushed Jul 31, 2024. [Video-LLaMA](https://github.com/DAMO-NLP-SG/Video-LLaMA) has 3.1k stars, 287 forks, and 69 open issues, last pushed Jun 4, 2024. Figures are from public GitHub metadata via [caffe's repository](https://github.com/BVLC/caffe) and [Video-LLaMA's repository](https://github.com/DAMO-NLP-SG/Video-LLaMA). | | [caffe](/tools/bvlc-caffe.md) | [Video-LLaMA](/tools/damo-nlp-sg-video-llama.md) | | --- | --- | --- | | Tagline | Caffe is a fast open framework for deep learning. | Instruction-tuned Audio-Visual Language Model for Video Understanding | | Stars | 34,573 | 3,141 | | Forks | 18,443 | 287 | | Open issues | 1,471 | 69 | | Language | C++ | Python | | Adopt for | Caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. | Video-LLaMA is an audio-visual language model that enhances video and audio understanding capabilities for language models. | | Persona | - | - | | Runtime | - | - | | License | Caffe is available under the BSD 2-Clause license. | BSD-3-Clause | | Categories | Computer Vision, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caffe](/tools/bvlc-caffe.md) | [Video-LLaMA](/tools/damo-nlp-sg-video-llama.md) | | --- | --- | --- | | Days since push | 732d | 804d | | Open issues (now) | 1.5k | 69 | | Stars delta | Unknown | +2 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/bvlc-caffe/trust.md) | [trust report](/tools/damo-nlp-sg-video-llama/trust.md) | ## Decision facts: caffe - **Pricing:** freemium - Free to use under open source licensing with no monetary charges. - **Adopt for:** Caffe is designed for deep learning tasks, especially those involving computer vision, and is written in C++ to ensure efficiency. - **License detail:** Caffe is available under the BSD 2-Clause license. ## Decision facts: Video-LLaMA - **Requirements:** Ensure access to compatible hardware for video and audio processing tasks.; Consider the availability of Chinese text representation as a potential advantage or limitation based on your project needs. - **Adopt for:** Video-LLaMA is an audio-visual language model that enhances video and audio understanding capabilities for language models. - **License detail:** BSD-3-Clause ## Choose when ### Choose caffe if… - caffe is primarily C++; Video-LLaMA is Python. - License: caffe is Other, Video-LLaMA is BSD-3-Clause. - Pricing: Free to use under open source licensing with no monetary charges.. - Tags unique to caffe: deep-learning, machine-learning, vision. - - You need a framework that supports high-performance convolutional networks particularly suited for image classification ### Choose Video-LLaMA if… - Video-LLaMA is primarily Python; caffe is C++. - License: Video-LLaMA is BSD-3-Clause, caffe is Other. - Requirements: Ensure access to compatible hardware for video and audio processing tasks.; Consider the availability of Chinese text representation as a potential advantage or limitation based on your project needs.. - Tags unique to Video-LLaMA: blip2, cross-modal-pretraining, large language models, llama. - When you need to process video content with instruction-tuned multimodal capabilities, especially when working with videos that require both visual and auditory analysis. ## When NOT to use caffe - - Your primary task involves natural language processing rather than computer vision challenges, where specialized frameworks might outperform Caffe - - You seek a framework that integrates seamlessly with Python for both training and inference, as Caffe relies heavily on C++ for its core operations ## When NOT to use Video-LLaMA - Do not use when the primary focus is on languages other than English and Chinese, as the model's representation capabilities outside these languages might be limited. - Avoid using Video-LLaMA if you require real-time audio processing in a deployment environment that does not support Vicuna-7B audio branch currently running on A10-24G GPUs. ## Common questions ### What is the difference between caffe and Video-LLaMA? caffe: Caffe is a fast open framework for deep learning.. Video-LLaMA: Instruction-tuned Audio-Visual Language Model for Video Understanding. See the comparison table for live GitHub stats and shared categories. ### When should I choose caffe over Video-LLaMA? Choose caffe over Video-LLaMA when caffe is primarily C++; Video-LLaMA is Python; License: caffe is Other, Video-LLaMA is BSD-3-Clause; Pricing: Free to use under open source licensing with no monetary charges.; Tags unique to caffe: deep-learning, machine-learning, vision; - You need a framework that supports high-performance convolutional networks particularly suited for image classification. ### When should I choose Video-LLaMA over caffe? Choose Video-LLaMA over caffe when Video-LLaMA is primarily Python; caffe is C++; License: Video-LLaMA is BSD-3-Clause, caffe is Other; Requirements: Ensure access to compatible hardware for video and audio processing tasks.; Consider the availability of Chinese text representation as a potential advantage or limitation based on your project needs.; Tags unique to Video-LLaMA: blip2, cross-modal-pretraining, large language models, llama; When you need to process video content with instruction-tuned multimodal capabilities, especially when working with videos that require both visual and auditory analysis. ### When should I avoid caffe? - Your primary task involves natural language processing rather than computer vision challenges, where specialized frameworks might outperform Caffe - You seek a framework that integrates seamlessly with Python for both training and inference, as Caffe relies heavily on C++ for its core operations ### When should I avoid Video-LLaMA? Do not use when the primary focus is on languages other than English and Chinese, as the model's representation capabilities outside these languages might be limited. Avoid using Video-LLaMA if you require real-time audio processing in a deployment environment that does not support Vicuna-7B audio branch currently running on A10-24G GPUs. ### Is caffe or Video-LLaMA more popular on GitHub? caffe has more GitHub stars (34,573 vs 3,141). Stars measure visibility, not whether either tool fits your constraints. ### Are caffe and Video-LLaMA open source? Yes - both are open-source projects on GitHub (caffe: Other, Video-LLaMA: BSD-3-Clause). ### Where can I find alternatives to caffe or Video-LLaMA? GraphCanon lists graph-backed alternatives at [caffe alternatives](/tools/bvlc-caffe/alternatives) and [Video-LLaMA alternatives](/tools/damo-nlp-sg-video-llama/alternatives) ([caffe markdown twin](/tools/bvlc-caffe/alternatives.md), [Video-LLaMA markdown twin](/tools/damo-nlp-sg-video-llama/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/bvlc-caffe-vs-damo-nlp-sg-video-llama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caffe or Video-LLaMA? caffe: Dormant. Video-LLaMA: 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 caffe and Video-LLaMA? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caffe trust report](/tools/bvlc-caffe/trust); [Video-LLaMA trust report](/tools/damo-nlp-sg-video-llama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bvlc-caffe`](/api/graphcanon/graph?tool=bvlc-caffe) - 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/_