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Found 36 Skills
Train a computer-vision model with the getitune library (the Geti training library) using its Python API or CLI. Use when a user wants to train, fine-tune, or evaluate a model with `create_engine(...)` and `engine.train()/engine.test()`, run `getitune train`/`getitune test`, pick or override a recipe under `getitune.recipe.<task>`, choose a device (cpu/gpu/xpu/cuda), warm-start from a checkpoint, or debug a training run. Covers classification, detection, instance/semantic segmentation, and keypoint detection.
NVPanoptix3D for panoptic 3D scene reconstruction from posed RGB images. Produces 3D panoptic segmentation (semantic, instance, and panoptic masks) with occupancy completion. Built on a VGGT backbone with a Mask2Former-style head and 3D frustum reconstruction. Use when training, evaluating, exporting, or running inference for a TAO NVPanoptix3D model. Trigger phrases include "train NVPanoptix3D", "panoptic 3D reconstruction", "3D scene segmentation", "occupancy completion".
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs them to learn visual representations; supports pretrain and finetune stages. Use when training, evaluating, exporting, or running inference for a TAO MAE backbone. Trigger phrases include "pretrain MAE", "self-supervised vision pretraining", "Masked Autoencoder", "Mask Auto-Encoder", "MAE fine-tune".
Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
Analyze images — segment objects, detect, run OCR, describe, and answer visual questions via fal.ai vision models.
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check, HuggingFace inspection and validation, codebase exploration, tao-core configuration and native trainer implementation, ONNX export plus TensorRT deploy integration, packaging and L0 testing, container-based end-to-end validation, and (conditional) accuracy/latency tuning. Supports classification, object detection, semantic / instance / panoptic segmentation, zero-shot detection, and depth estimation.
Expert guidance for working with Hugging Face Transformers library for NLP, computer vision, and multimodal AI tasks.
Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform, cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming, epochs, batch, imgsz, devices, augmentation, multi-GPU, resumes, results, and fixing OOM, NaN loss, low mAP, or overfitting. For hyperparameter search and systematic improvement loops, see yolo-tuning.
Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them to pixel-space bounding boxes via a VLM. Use when the user wants to ground captions to bboxes, generate phrase-grounded annotations, auto-label images for grounding, or run the image_grounding pipeline. Triggers include 'image grounding', 'phrase grounding', 'ground captions', 'auto-label image grounding', 'image_grounding'.
Migrate vision/detection/segmentation small models to Ascend NPU, covering the full workflow: model structure analysis, migration verification, performance profiling, and optimization. Based on torch_npu and msprof Use this skill when the user wants to: (1) migrate encoder-only models like ResNet, YOLO, UNet to Ascend NPU, (2) analyze model structure for migration feasibility, (3) verify model inference on NPU, (4) identify performance bottlenecks and get optimization suggestions Trigger: user mentions "migrate", "migration", "Ascend", "NPU", "YOLO", "ResNet", "encoder-only", "detection", "segmentation", "adaptation", "adapt", "迁移", "昇腾迁移", "小模型", "适配", "昇腾适配", "GPU迁移", "NPU适配", "适配NPU", "适配昇腾"
Build on-device AI into React Native apps using ExecuTorch. Provides hooks for LLMs, computer vision, OCR, audio processing, and embeddings without cloud dependencies. Use when building AI features into mobile apps - AI chatbots, image recognition, speech processing, or text search.
subject segmentation, VNGenerateForegroundInstanceMaskRequest, isolate object from hand, VisionKit subject lifting, image foreground detection, instance masks, class-agnostic segmentation, VNRecognizeTextRequest, OCR, VNDetectBarcodesRequest, DataScannerViewController, document scanning, RecognizeDocumentsRequest