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RF

RF-DETR, SOTA Real-Time Object Detection Model

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RF-DETR, SOTA Real-Time Object Detection Model

The Roboflow ML team has been actively working on RF-DETR, a real-time, transformer-based object detection model architecture. The model architecture is now public and open source (Apache 2.0). RF-DETR-large is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark [1] and achieve strong speeds compared to other models at the base size. On an NVIDIA T4 GPU, RF-DETR-Base achieves 160 FPS. RF-DETR is designed to transfer well to identify real-world objects that aren’t usually found in common training datasets, such as those found in industrial environments, wildlife settings, lab images, thermal, and novel research areas. This is measured using the new RF100-VL benchmark [2] developed in partnership with researchers from CMU. If you try out the model, let us know! We have a fine-tuning guide to get you started with training models. [3] [1]: https://cocodataset.org [2]: https://github.com/roboflow/rf100-vl [3]: https://blog.roboflow.com/train-rf-detr-on-a-custom-dataset/

Visit productView on Hacker NewsMar 21, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training, active · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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