Huggingface Vision Trainer

This handles the full pipeline for training vision models on Hugging Face Jobs cloud GPUs: object detection (D-FINE, RT-DETR, DETR, YOLOS), image classification (timm models like MobileNetV3, ResNet, ViT), and SAM/SAM2 segmentation. It includes dataset validation scripts that check format compatibility before you burn GPU credits, automatic bbox preprocessing that converts between COCO and Pascal VOC formats, and augmentation with Albumentations. The workflow is solid: validate your dataset structure, choose between quick test runs or full training, submit the job with proper token secrets, and get your model pushed to the Hub. One heads up: the default 30-minute timeout is way too short for real training, so you'll need to bump that based on dataset size.

skills.sh installs: 2K
GitHub stars: 11.3K
last updated: Aug 21, 2026

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Install

$ npx -y skills add huggingface/skills --skill huggingface-vision-trainer --agent claude-code

Installs into .claude/skills of the current project.

Files

Vision Model Training on Hugging Face Jobs

Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub.

When to Use This Skill

Use this skill when users want to:

Fine-tune object detection models (D-FINE, RT-DETR v2, DETR, YOLOS) on cloud GPUs or local
Fine-tune image classification models (timm: MobileNetV3, MobileViT, ResNet, ViT/DINOv3, or any Transformers classifier) on cloud GPUs or local
Fine-tune SAM or SAM2 models for segmentation / image matting using bbox or point prompts
Train bounding-box detectors on custom datasets
Train image classifiers on custom datasets
Train segmentation models on custom mask datasets with prompts
Run vision training jobs on Hugging Face Jobs infrastructure
Ensure trained vision models are permanently saved to the Hub