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NVIDIA AI Platform · Computer vision
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | YOLO 2026 — state-of-the-art real-time object detection. An agent skill from SharpAI/DeepCamera. | SharpAI/ | 3.1k | — | ~1.5k | Automated safety check: Pass | MIT | 23 days ago |
| 2 | Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling. | NVIDIA/ | 3.6k | — | ~5k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 3 | Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download. | NVIDIA/ | 3.6k | — | ~4.7k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 4 | Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV. | NVIDIA/ | 3.6k | — | ~2.7k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 5 | Runs TAO Data Services gap analysis that compares ground-truth and predicted boxes to find weak images by per-class recall, precision and AP50. | NVIDIA/ | 3.6k | — | ~1.8k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 6 | Compares a vision-language model's yes/no predictions with ground truth and writes the false-positive and false-negative cases to a JSONL file with a summary report. | NVIDIA/ | 3.6k | — | ~1.3k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 7 | Turns a parquet of image file paths into a parquet of embeddings with CLIP, SigLIP or a TAO checkpoint, using the TAO Data Services container, ahead of neighbor mining. | NVIDIA/ | 3.6k | — | ~2k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 8 | Computer vision engineering skill for object detection, image segmentation, and visual AI systems. | alirezarezvani/ | 28k | 1 repo | ~3.2k | Automated safety check: Pass | MIT | 1 mo ago |
| 9 | Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap analysis, SigLIP embedding of weak images… | NVIDIA/ | 3.6k | — | ~3.3k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 10 | A skill your agent uses to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors… | NVIDIA/ | 3.6k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 11 | NVIDIA DeepStream SDK development with Python pyservicemaker API. | NVIDIA/ | 3.6k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 12 | A skill your agent uses when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether… | NVIDIA/ | 3.6k | — | ~4.7k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 13 | CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. | NVIDIA/ | 3.6k | — | ~4k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 14 | Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container when no dedicated TAO model skill matches. | NVIDIA/ | 3.6k | — | ~4.9k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 15 | BEVFusion for multi-sensor 3D object detection. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~3.4k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 16 | Co-DETR (CoDINO) for object detection. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~4.8k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 17 | DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. | NVIDIA/ | 3.6k | — | ~2.8k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 18 | Grounding DINO for open-set object detection. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~3.9k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 19 | PyTorch-based TAO image classification. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 20 | PointPillars for 3D object detection from LiDAR point clouds. | NVIDIA/ | 3.6k | — | ~4k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 21 | RT-DETR (Real-Time DEtection TRansformer) for 2D object detection. | NVIDIA/ | 3.6k | — | ~4.5k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 22 | Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). | NVIDIA/ | 3.6k | — | ~4.5k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 23 | Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. | borghei/ | 891 | — | ~1.8k | Automated safety check: Pass | MIT | 3 days ago |
| 24 | Universal VLM (vision-language understanding model) training guide for Huawei Ascend NPU using MindSpeed-MM. | ascend-ai-coding/ | 174 | — | ~5.2k | Automated safety check: Pass | No licence | today |
| 25 | Build machine vision inspection systems with MATLAB Visual Inspection Toolbox. | matlab/ | 1.1k | — | ~3.1k | Automated safety check: Pass | Unknown | 2 days ago |