Explorez les skills IA publiés de voxel51 et consultez leurs instructions source.
Skills indexés
9Étoiles des dépôts des skills indexés
99Forks du dépôt
9Moyenne des étoiles par skill indexé
11Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classif...
Finds duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when deduplicating datasets, finding similar images, or removing redundant samples.
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
Develops custom FiftyOne plugins (operators and panels) from scratch. Use when creating plugins, extending FiftyOne with custom operators, building interactive panels, or integrating external APIs.
Triages FiftyOne GitHub issues by categorizing as fixed, won't fix, not reproducible, or still valid. Use when reviewing GitHub issues, triaging bugs, or closing stale issues in the voxel51/fiftyone repository.
Exports FiftyOne datasets to standard formats (COCO, YOLO, VOC, CVAT, CSV, etc.). Use when converting datasets, exporting for training, creating archives, or sharing data in specific formats.
Imports datasets into FiftyOne with automatic format detection. Supports all media types (images, videos, point clouds), label formats (COCO, YOLO, VOC, KITTI), and multimodal grouped datasets. Use when importi...
Writes Python code following FiftyOne's official conventions. Use when contributing to FiftyOne, developing plugins, or writing code that integrates with FiftyOne's codebase.
Run ML model inference (YOLO, YOLOv8, CLIP, SAM, Detectron2, etc.) on FiftyOne datasets. Use when running models, applying detection, classification, segmentation, embeddings, or any model prediction task. Also...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.