Automated Vision Model Training on Hugging Face Jobs

---
source: "https://github.com/huggingface/skills/tree/main/skills/huggingface-vision-trainer"
name: hugging-face-vision-trainer
description: Train or fine-tune vision models on Hugging Face Jobs for detection, classification, and SAM or SAM2 segmentation.
risk: unknown
---

# 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

## Related Skills

- **`hugging-face-jobs`** — General HF Jobs infrastructure: token authentication, hardware flavors, timeout management, cost estimation, secrets, environment variables, scheduled jobs, and result persistence. **Refer to the Jobs skill for any non-training-specific Jobs questions** (e.g., "how do secrets work?", "what hardware is available?", "how do I pass tokens?").
- **`hugging-face-model-trainer`** — TRL-based language model training (SFT, DPO, GRPO). Use that skill for text/language model fine-tuning.

## Local Script Execution

Helper scripts use PEP 723 inline dependencies. Run them with `uv run`:
```bash
uv run scripts/dataset_inspector.py --dataset username/dataset-name --split train
uv run scripts/estimate_cost.py --help
```

## Prerequisites Checklist

Before starting