Sam V2.1 Hiera Large
SAM v2, the next-gen segmentation model from Meta AI, revolutionizes computer vision. Building on SAM's success, it excels at accurately segmenting objects in images, offering robust and efficient solutions for various visual contexts.
Resources to get you started
Everything you need to know to get the most out of Sam V2.1 Hiera Large
SAM v2.1 Hiera Large
SAM v2.1 (Segment Anything Model 2.1) represents the next evolution in promptable visual segmentation by Meta AI, delivering more accurate and efficient mask generation for a wide range of image types and contexts.
Model Information
- •Architecture: SAM v2.1 extends the original SAM framework with optimized Hiera-based encoders for higher accuracy and speed.
- •Flexible Outputs: Supports overlay images, polygon coordinates, COCO RLE encodings, and individual PNG masks.
How to Use the Model
- •Upload an image (JPG, PNG, or WebP) — either as a URL or base64 string.
- •Configure output options:
- •make_overlay – Generate a visual overlay of masks on the input image.
- •save_polygons – Return polygon coordinates for each segmented region.
- •save_rle – Export COCO RLE encodings for mask data.
- •save_pngs – Save individual masks as PNGs in a ZIP file.
- •Adjust advanced parameters for quality and precision:
- •points_per_side, points_per_batch, pred_iou_thresh, stability_score_thresh, min_mask_region_area, nms_iou_thresh, max_masks, polygon_epsilon, tile_size, tile_stride.
- •Click “Generate” to obtain segmentation results.
Use Cases
- •Assisted Image Labeling: Speeds up dataset annotation with automatic mask proposals.
- •AR/VR Applications: Enables precise object isolation for immersive environments.
- •Autonomous Vehicles: Supports accurate perception and obstacle detection.
- •Environmental Monitoring: Segments satellite and aerial imagery for analysis.
- •Industrial & Sonar Imaging: Identifies regions of interest in complex visual data.
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