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Detect objects in videos locally on iPhone using Roboflow's real-time detection transformer (RF-DETR) in SwiftUI + Core AI. Run the model on iOS for free, no API calls.👇 - - -

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Amos Gyamfi

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12,986 görüntüleme • 1 ay önce •via X (Twitter)

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we are introducing a state-of-the-art real-time object detection model, RF-DETR RF-DETR outperforms all existing object detection models on real world datasets and is the first real-time model to achieve 60+ Average Precision on COCO talked w/ NVIDIA about it at GTC:
2:31

we are introducing a state-of-the-art real-time object detection model, RF-DETR RF-DETR outperforms all existing object detection models on real world datasets and is the first real-time model to achieve 60+ Average Precision on COCO talked w/ NVIDIA about it at GTC:

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RF-DETR, the state-of-the-art model series for real-time object detection, can now run 100% locally in your browser on WebGPU with 🤗 Transformers.js v4! The models are Apache-2.0 licensed, making them a perfect fit for both personal and commercial applications. Try the demo 👇
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RF-DETR, the state-of-the-art model series for real-time object detection, can now run 100% locally in your browser on WebGPU with 🤗 Transformers.js v4! The models are Apache-2.0 licensed, making them a perfect fit for both personal and commercial applications. Try the demo 👇

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for Basketball AI, I want to read jersey numbers and map them to player names I'm using RF-DETR + SmolVLM2 combo to do it - detect jersey numbers in real-time with RF-DETR - crop number regions - OCR the numbers with SmolVLM2 computer vision + sports:
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for Basketball AI, I want to read jersey numbers and map them to player names I'm using RF-DETR + SmolVLM2 combo to do it - detect jersey numbers in real-time with RF-DETR - crop number regions - OCR the numbers with SmolVLM2 computer vision + sports:

SkalskiP

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i made a computer vision system to monitor the situation - live webcam feed and roboflow RF-DETR for object detection - inputs are fed into SmolVLM which describes what it sees in one sentence - dataviz with vanilla js all running locally in real-time on my macbook air m2
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i made a computer vision system to monitor the situation - live webcam feed and roboflow RF-DETR for object detection - inputs are fed into SmolVLM which describes what it sees in one sentence - dataviz with vanilla js all running locally in real-time on my macbook air m2

AA

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SwiftUI isn’t native and here’s why SwiftUI apps may sometimes feel janky. It ditches the key element that made animations run so smoothly on iOS since the original iPhone: Core Animation. In SwiftUI animations run in-app without CA backing. Block the main thread: SwiftUI ball stalls while UIKit’s keeps moving on the render server.
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SwiftUI isn’t native and here’s why SwiftUI apps may sometimes feel janky. It ditches the key element that made animations run so smoothly on iOS since the original iPhone: Core Animation. In SwiftUI animations run in-app without CA backing. Block the main thread: SwiftUI ball stalls while UIKit’s keeps moving on the render server.

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DART: Detect Anything in Real Time Detect Anything in Real Time: Real-time object detection using frontier object detection models. Training-free framework that converts SAM3 into a real-time multi-class open-vocabulary detector. Achieves 55.8 AP on COCO val2017 (80 classes) at 15.8 FPS (4 classes, 1008px) on a single RTX 4080.
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DART: Detect Anything in Real Time Detect Anything in Real Time: Real-time object detection using frontier object detection models. Training-free framework that converts SAM3 into a real-time multi-class open-vocabulary detector. Achieves 55.8 AP on COCO val2017 (80 classes) at 15.8 FPS (4 classes, 1008px) on a single RTX 4080.

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i gave a voice to my jarvis system everything running in real-time using open source models - roboflow RF-DETR for object detection - gemma 4 for scene summarization - kokoro text-to-speech - live in the browser using transformers js prompt: "you are a dystopian science fiction narrator in a cyberpunk world. describe the scene in one sentence"
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i gave a voice to my jarvis system everything running in real-time using open source models - roboflow RF-DETR for object detection - gemma 4 for scene summarization - kokoro text-to-speech - live in the browser using transformers js prompt: "you are a dystopian science fiction narrator in a cyberpunk world. describe the scene in one sentence"

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THIS GUY STACKED MAC MINIS TO RUN AI LOCALLY AND IT CODES FOR FREE no API. no subscription. no cloud. what he's running: - EXO across multiple Mac Minis - Qwen 2.5 Coder 7B locally - Zed editor connected to the model - GPU usage visible in real time the math: - Mac Mini M4: $599 one-time - electricity: $3/month - API costs: $0 vs $459/month in subscriptions if you want Claude Code to run at this level ClaudeKit gives it the structure to do that ( full breakdown in the article below
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THIS GUY STACKED MAC MINIS TO RUN AI LOCALLY AND IT CODES FOR FREE no API. no subscription. no cloud. what he's running: - EXO across multiple Mac Minis - Qwen 2.5 Coder 7B locally - Zed editor connected to the model - GPU usage visible in real time the math: - Mac Mini M4: $599 one-time - electricity: $3/month - API costs: $0 vs $459/month in subscriptions if you want Claude Code to run at this level ClaudeKit gives it the structure to do that ( full breakdown in the article below

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you can literally build an app like this during your coffee break rebuilt the core concept in under 30 minutes using less than 10 credits > iteractive map > data stored in Supabase >native ios app running on a real iphone the hard part isn’t building it anymore it’s getting enough eyeballs on it built on Superapp AI ↓
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you can literally build an app like this during your coffee break rebuilt the core concept in under 30 minutes using less than 10 credits > iteractive map > data stored in Supabase >native ios app running on a real iphone the hard part isn’t building it anymore it’s getting enough eyeballs on it built on Superapp AI ↓

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another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.
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another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

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i trained a computer vision model to train me at tennis it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball how this works: - input video from an iphone - CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model - video annotation using python + openCV goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique more to come!

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Meta’s Segment Anything Model (SAM) 3.1 is now available on Meta Model API, giving developers a fast and lightweight model for detection, segmentation and tracking in a single call on inference tuned for SAM 3.1's architecture. Use a short phrase to find objects in images and video. One API call returns detections, pixel-precise segmentation masks, and identity-preserving video tracks. Learn more and start building:

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