Computer Vision · Explorer
Recognize (SAM3 Experiments).
Short experiments running Meta's SAM3 (Segment Anything Model 3) on images with text prompts, plus a small script to visualize the predicted masks.
PythonPyTorchSAM3PIL
§ 01The problem.
Try Meta's SAM3 for promptable, open-vocabulary segmentation: point it at an image, give it a text prompt, and see what it can segment, as a hands-on way to understand the model rather than a product.
§ 02How it works.
- Pulls in Meta's SAM3 as a git submodule and uses its model builder and image processor directly, no reimplementation.
- A short run script builds the SAM3 image model, sets an image, applies a text prompt (for example 'person'), and reads back masks, boxes, and confidence scores.
- A companion script visualizes the predicted masks over the image.
§ 03Results and impact.
- A small, working reference for promptable segmentation with SAM3: prompt an image and get masks, boxes, and scores in a few lines.
- Exploratory by design, this is a learning experiment with Meta's model, not an original system.