Originally created by: EHxuban11
Originally owned by: EHxuban11
Q2: april, may, june
The goal of this Marketing Plan is for LibreYOLO to go mainstream in Q2. The strategy is based in common sense as well as some creative ideas.
The source of truth for the Q2 marketing is this Issue. The subcomponents of this plan as well as tasks are going to be sub-issues and sub-sub-issues reespectively. The sub-components are ordered by importance:
LibreYOLO’s Q2 marketing goal, covering April through June, is to make the project mainstream. The strategy combines common-sense with a few creative initiatives. The source of truth for Q2 marketing is this issue, and the different parts of the plan should be organized as sub-issues, with their tasks represented as sub-sub-issues. These subcomponents are ordered by importance.
The first priority is positioning Vision Analysis (visionanalysis.org). Vision Analysis is intended to become for computer vision what Artificial Analysis is for large language models: the canonical destination for benchmarks, comparisons, and model evaluation. For anyone unfamiliar with Artificial Analysis, it is widely used as the go-to website for checking LLM benchmarks, and it is often cited because of its overall scoring framework, the Artificial Analysis Intelligence Index. Strategically, the goal is to establish Vision Analysis as the equivalent reference point for computer vision.
This positioning is possible because there is a major gap in the market. Today, there is no definitive, in-depth way to compare YOLO models or computer vision models more broadly. Even simple searches such as “YOLOv8 vs YOLOv9 on NVIDIA Orin” reveal how limited the ecosystem still is: in many cases, there may be only a single comparison available, often using outdated hardware such as a T4 GPU, and usually reporting little more than mAP. Beyond mAP, and occasionally latency on outdated T4 GPUs, meaningful comparative information is very difficult to find. There is no standard way to compare models across hardware platforms, to select hardware based on model behavior, or to understand a model in a more complete way.
In practice, a model should be evaluated through a richer set of dimensions, including its learning capacity, inference performance, size, FLOPs, and mAP scores. This is especially important for areas such as small-object performance, where a single headline metric cannot capture real-world usefulness. mAP alone is not enough, even though it is still the number most commonly reported. To address that, we have created a more holistic benchmark metric modeled after the Artificial Analysis Intelligence Index. Our version is called the Vision Analysis v1 Score, or just VA v1.
Beyond that headline score, the Vision Analysis website already captures far more detailed information than other benchmarking sites. It measures preprocessing speed, inference speed, postprocessing speed, and end-to-end latency. It also tracks metrics such as mAP@50, latency-related parameters, GFLOPs, and efficiency indicators such as mAP per GFLOP. In addition, each model includes a detailed model card with links to the original paper and repository. The database also contains benchmark results across multiple runtimes and precisions, including PyTorch, ONNX, TensorRT, and OpenVINO, as well as across a broad range of hardware platforms such as NVIDIA Orin devices, Raspberry Pi boards, RTX GPUs, A100s, Apple Silicon systems, and DGX-class hardware. There is simply no other website in this category that offers this level of depth, and this is only the beginning.
This section of the plan should be divided into several tasks. First, we need to build a benchmarking suite for LibreYOLO that can generate all the data required to populate the Vision Analysis databases. Second, we need to run that suite across as many hardware targets, inference engines, and models as possible. If we face constraints, the priority should be PyTorch, ONNX, and TensorRT, along with the most important real-time computer vision platforms, especially the NVIDIA Orin family.
Third, we should generate comparison articles between different models. This can be done in two phases. The first phase is a programmatic draft process in which a script compares two models from the database and produces a structured draft. An LLM can then turn that draft into a polished article while preserving the correctness of the benchmark data. If time allows, the same method can be extended to hardware comparisons, inference-platform comparisons, and precision comparisons. For example, if we have the relevant benchmark data, we could publish articles answering questions such as whether YOLOv9 runs faster on a Raspberry Pi 5, an NVIDIA Orin, or an NVIDIA Xavier.
LibreYOLO.com is the main website of LibreYOLO. It contains the landing page that explains the project, and it currently also hosts the docs. In Q2, we are going to open new content spaces designed to attract users through SEO and GEO. In particular, this means publishing blog posts and articles, especially migration guides. We have identified a number of computer vision repositories that are effectively dead or stale, despite having attracted a lot of attention and users in the past. This creates an opportunity for LibreYOLO, to capture users who are still searching for these projects, but are now looking for a maintained alternative. We are going to write migration guides from the following libraries to LibreYOLO:
Title example: “YOLO-NAS is dead: how to migrate to LibreYOLO in 5 minutes.”
Reddit is a great place for computer vision projects as demonstrated in the past. This library got its traction from Reddit and some of the current contributors met the project in Reddit. We have 1 post in the computer vision subreddit, but we believe that there is more we can do. This section is dedicated to explaining and detailing this.
We're going to implement use case marketing.
To illustrate what a use case is, for example YOLO blur faces:
Each use case is going to comprise:
Integrations that put libreyolo in front of new audiences. Highest priority are AXELERA and FiftyOne.
We have noticed that when asking an alternative to select an alternative to a computer vision library to Claude Code, it often looks at tool aggregator sites such as G2, Toolify, SourceForge, Slashdot Software SaaS Hub. Alternative to .Product Hunt, La Forge
We believe, therefore, that we need to add our tool into these aggregator sites.
