Segment objects in images using point, box, or mask prompts.
npx skills add https://github.com/graniet/kheish --skill segment-anything-graniet
Please help me install this Agent Skill. Skill: segment-anything Source: https://github.com/graniet/kheish/tree/main/skills/mlops/models/segment-anything Command: npx skills add https://github.com/graniet/kheish --skill segment-anything-graniet
This Skill eliminates the need for time-consuming custom model training or manual annotation to segment objects in images, making zero-shot image segmentation accessible for any use case without domain-specific expertise.
Use the segment-anything skill to generate segmentation masks for all products in the attached e-commerce product photo 'product_001.jpg' and save the masks as individual PNG files with transparent backgrounds.
High-intent search queries and answers about installing and using this skill.
Zero-shot image segmentation segments any object in images without task-specific fine-tuning. You can use Meta AI's Segment Anything Model with point, box, or mask prompts to generate accurate masks instantly for any image domain.
Automatic mask generation instantly produces masks for all objects in an image. This supports dataset annotation, object detection pipelines, and content creation workflows without requiring manual tracing or custom model training.
Zero-shot segmentation applies to medical image analysis and satellite imagery processing without domain-specific expertise. A medical research team can automatically segment regions of interest in CT scans without training a custom segmentation model.
Running the Segment Anything Model requires the segment-anything library and PyTorch. You need CUDA-enabled compute for GPU acceleration, but the model also supports running on standard CPU compute.
E-commerce product image extraction uses zero-shot segmentation to separate products from backgrounds. You can generate segmentation masks for all products in a photo and save them as individual PNG files with transparent backgrounds.
Interactive annotation uses point clicks and bounding boxes to guide segmentation for precise results. Flexible prompting with previous masks allows targeted mask generation without needing task-specific model training or fine-tuning.
InverterNetworkSegment objects in images using point, box, or automatic prompts.
MarbleSodasGenerate object masks from point, box, or mask prompts without fine-tuning.
automatedigitalSegment arbitrary objects in images using point, box, or mask prompts.
nahisahoFine-tune pre-trained models on domain-specific scientific datasets with transfer learning workflows.
