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.
- ROLE
- Explorer
- PERIOD
- 2026
- DOMAIN
- Computer vision
- CODE
- GitHub ↗
OVERVIEW
A small set of experiments with Meta's SAM3 (Segment Anything Model 3) for promptable, open-vocabulary segmentation. SAM3 is included as a git submodule; two short Python scripts use it directly. One builds the image model, sets an image, applies a text prompt, and returns masks, boxes, and scores; the other visualizes the predicted masks. It is a hands-on exploration of the model rather than an original project.
ARRIVED AS
“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.”
WHAT I BUILT
- 01Pulls in Meta's SAM3 as a git submodule and uses its model builder and image processor directly, no reimplementation.
- 02A 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.
- 03A companion script visualizes the predicted masks over the image.
WHAT CHANGED
- 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: a learning experiment with Meta's model, not an original system.