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hugging-face-vision-trainer

Train vision models on Hugging Face Jobs with hub-pushed artifacts.

1|Updated Feb 20, 2026
Search Tags:#object-detection#image-classification#huggingface-jobs#dataset-preparation#vision-training#sam-segmentation#hub-persistence
One-click install
npx skills add https://github.com/BlackRoad-OS-Inc/blackroad-operator --skill hugging-face-vision-trainer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-vision-trainer
Source: https://github.com/BlackRoad-OS-Inc/blackroad-operator/tree/main/agents/skills/skills/hugging-face-vision-trainer
Command: npx skills add https://github.com/BlackRoad-OS-Inc/blackroad-operator --skill hugging-face-vision-trainer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers>=5.2.0, accelerate>=1.1.0, albumentations>=1.4.16, timm, datasets>=4.0, torchmetrics, pycocotools, trackio, huggingface_hub, monai, torchvision, evaluate, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Trains and fine-tunes object detection, image classification, and SAM/SAM2 vision models on Hugging Face Jobs cloud GPUs, removing the need for local GPU setup and enabling scalable experimentation.

Core Features & Use Cases

  • Supports object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm-based models, ViT, ResNet), and SAM/SAM2 segmentation with prompt-based fine-tuning.
  • Provides COCO-format dataset preparation, Albumentations-based augmentation, evaluation metrics (mAP, mAR), and Hub persistence with Trackio monitoring.
  • Includes ready-to-run training scripts, dataset validation utilities, and cost estimation to help plan hardware and budgets.

Quick Start

Run a full training job on Hugging Face Jobs for vision models by selecting a model and dataset, then push the trained model to the Hub.

Frequently Asked Questions about hugging-face-vision-trainer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train object detection models on Hugging Face Jobs without a local GPU?▼

To train vision models on Hugging Face Jobs, you need the transformers, accelerate, and albumentations libraries. The workflow requires COCO-format datasets for object detection and token-based authentication to ensure reliable, auditable cloud execution and Hub persistence.

Can I fine-tune SAM and SAM2 segmentation models using Hugging Face Jobs?▼

Hugging Face Jobs supports image classification training for timm-based models, ViT, and ResNet architectures. The workflow integrates Albumentations for data augmentation and torchmetrics for evaluation, allowing you to push trained classification artifacts directly to the Hub.

What is the best way to prepare COCO datasets for vision model training on Hugging Face?▼

Yes, you can estimate hardware costs and plan budgets for vision training jobs. The workflow includes cost estimation utilities and explicit hardware selection to help plan cloud GPU usage for object detection, classification, and SAM segmentation tasks.

How do I evaluate mAP and mAR metrics for object detection models trained in the cloud?▼

Yes, Hub persistence is enforced using token-based authentication and Trackio monitoring. This ensures your trained vision models, including object detection and SAM segmentation artifacts, are reliably pushed to the Hugging Face Hub with auditable run metadata.

Do I need albumentations and pycocotools to run SAM segmentation workflows on Hugging Face Jobs?▼

You should not use this workflow if your datasets lack proper COCO-format validation or if you cannot provide token-based authentication for Hub persistence. The skill enforces dataset checks and explicit script metadata to ensure auditable, reliable cloud training execution.

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