Uploaded August 2026 | Updated September 2026, 2 weeks ago

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🛠️ Computer vision workflows without code → pysource.com
👉 AI Vision courses + Community → skool.com/ai-vision-academy

In this video, I test SAM 3 for real-time industrial computer vision and conveyor-belt inspection.

SAM 3 can segment objects from a simple text prompt, without first collecting a dataset or training a custom model. I test it on several conveyor-belt scenarios, including:

• Packages
• Tomatoes
• Green tomatoes
• Lemons
• Palm fruit
• Rocks and mixed objects

The tests are performed inside VisoNode, my local no-code computer vision platform.

I also discuss:

• Text-prompt segmentation
• Real-time performance
• Confidence threshold adjustments
• Small-object detection
• Industrial inspection use cases
• GPU and CPU performance
• TensorRT optimization
• Using SAM 3 to prototype before training a production model

SAM 3 is a powerful option for quickly testing whether an industrial computer vision idea is feasible. For production deployment, it can then be used to help annotate data and train a smaller, faster custom model such as YOLO or RF-DETR.

🌐 Learn more about VisoNode → pysource.com
🌐 More Computer Vision Resources → pysource.com

#ComputerVision #SAM3 #IndustrialInspection #MachineVision #ArtificialIntelligence #ObjectSegmentation #VisoNode #NoCode #Manufacturing #OpenCV