List of Meta Detectron2 Customers
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Since 2010, our global team of researchers has been studying Meta Detectron2 customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Meta Detectron2 for Computer Vision from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Meta Detectron2 for Computer Vision include: Meta, a United States based Media organisation with 75945 employees and revenues of $164.50 billion, BINGO Industries, a Australia based Professional Services organisation with 650 employees and revenues of $485.0 million, LeafMachine2, a United States based Life Sciences organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Meta Detectron2, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Meta Detectron2 customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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BINGO Industries | Professional Services | 650 | $485M | Australia | Meta | Meta Detectron2 | Computer Vision | 2021 | n/a | In 2021, BINGO Industries implemented Meta Detectron2 as the core of a Safety Zone AI Model integrated into its CCTV system at the MPC 2 facility in Eastern Creek, addressing tipping floor safety within the Computer Vision category. The deployment of Meta Detectron2 is fielded as a real time computer vision pipeline that inspects live camera feed from an MPC 2 camera, and the project is explicitly scoped to the Eastern Creek site as the initial operational installation. The implementation configures Meta Detectron2 to identify humans and moving vehicles as discrete object classes, and to apply a dynamic 10 metre exclusion zone around moving vehicles. The system triggers immediate alerts when a human enters the exclusion zone while a vehicle is in motion, and it has been further programmed to detect specific waste site conditions such as drivers exiting vehicles and whether the bucket of a front end loader is lowered to the ground, reflecting category aligned capabilities for object detection and scene understanding. Operationally the Safety Zone AI Model is integrated directly with the facility CCTV infrastructure, consuming real time video streams from MPC 2 cameras and emitting alert events into on site safety monitoring workflows. The current operational coverage is the MPC 2 facility at Eastern Creek, with BINGO stating intent to continue development that could extend Safety Zone capability to other BINGO facilities across its operations. Governance and process changes include automated exclusion zone enforcement logic embedded in the detection model, and a shift in tipping floor safety oversight toward sensor driven alerts and recorded footage for incident review. BINGO reports that Safety Zone has had strong success in alerting when humans enter exclusion zones, and ongoing development is focused on adapting the Meta Detectron2 based solution to the operational nuances of waste facility environments. | |
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LeafMachine2 | Life Sciences | 10 | $1M | United States | Meta | Meta Detectron2 | Computer Vision | 2023 | n/a | In 2023 LeafMachine2 implemented Meta Detectron2 for instance segmentation of leaves as part of an end-to-end pipeline to extract morphological traits from digitized herbarium specimens. The LeafMachine2 deployment is an academic research tooling effort in North America focused on biology and specimen digitization, and it uses Meta Detectron2 within the Computer Vision category to produce per-leaf segmentation masks that feed downstream trait extraction workflows. The implementation configures Meta Detectron2 for supervised instance segmentation, incorporating annotated mask datasets, training and validation splits, and inference pipelines that emit object masks and bounding boxes for each specimen image. Standard Computer Vision workflows such as data augmentation, model checkpointing, and evaluation metrics for mask quality are applied to support reproducible training and model selection, and Meta Detectron2 is cited explicitly for segmentation in published work arising from the project. Operationally the work targets biology research and specimen digitization processes within North America and is executed as a compact research implementation appropriate to a small organization. Governance emphasizes reproducible pipelines and published code and models to ensure traceability of segmentation outputs into the morphological trait extraction stages, and the project reports improved automated trait extraction throughput and scalability driven by Meta Detectron2 segmentation. | |
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Meta | Media | 75945 | $164.5B | United States | Meta | Meta Detectron2 | Computer Vision | 2019 | n/a | In 2019 Meta deployed Meta Detectron2 as an internal production computer-vision library. Meta Detectron2 is used by Meta to support object detection and instance segmentation workloads and is categorized under Computer Vision. Meta Detectron2, developed at Facebook AI Research, encapsulates core Computer Vision capabilities including model definitions, training pipelines, and inference components that feed production feature implementations. The library is employed inside Meta’s research to production pipeline to operationalize models developed by FAIR into product code paths. The deployment is an internal production implementation that has been used to power product features such as the Portal Smart Camera in the United States. Operational ownership sits at the intersection of computer vision research and product engineering teams, with the implementation documented by Meta and FAIR and corroborated by independent reporting. Governance and rollout followed an internal research to production workflow, with documented usage and implementation notes available from Meta/FAIR. The narrative focuses on the technical role of Meta Detectron2 within Meta’s Computer Vision stack and its application to production computer-vision features. |
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