What is huggingface-vision-trainer?

A training tool for computer-vision models, including object detectors, image classifiers, and image-segmentation models. It can run training on Hugging Face's managed cloud GPUs and save results to the Hugging Face Hub.

Use it to fine-tune detectors, classifiers, or segmentation models on custom image datasets, including COCO-format data and mask-based datasets.

It removes the need to set up a local GPU for supported vision-training jobs. It also covers common dataset formats and model-training tasks in one workflow.

huggingface-vision-trainer — Skill for Codex by PracticalSwan

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add PracticalSwan/agent-skills --skill huggingface-vision-trainer

Clone the repo

git clone --depth 1 https://github.com/PracticalSwan/agent-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-vision-trainer/github.svg)](https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-vision-trainer)

Your own site

<a href="https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-vision-trainer"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-vision-trainer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-vision-trainer"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-vision-trainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 194 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 8,049 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 89% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00194	$0.08049
Opus 5	$0.00097	$0.04025
Sonnet 5	$0.00039	$0.01610
Haiku 4.5	$0.00019	$0.00805

Measured yesterday against content hash 22d97d6eba85, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

huggingface-vision-trainer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/dataset_inspector.py, scripts/estimate_cost.py, scripts/image_classification_training.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

89% identical to huggingface-vision-trainer — 60 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

huggingface-vision-trainer/SKILL.md · 640 lines

How it starts

The opening of the file, as written. The whole thing — 640 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Local Script Execution

Helper scripts use PEP 723 inline dependencies. Run them with uv run:

uv run scripts/dataset_inspector.py --dataset username/dataset-name --split train
uv run scripts/estimate_cost.py --help

Prerequisites Checklist

Before starting any training job, verify:

Account & Authentication

Hugging Face Account with Pro, Team, or Enterprise plan (Jobs require paid plan)
Authenticated login: Check with hf_whoami() (tool) or hf auth whoami (terminal)
Token has write permissions
MUST pass token in job secrets — see directive #3 below for syntax (MCP tool vs Python API)

Dataset Requirements — Object Detection

Dataset must exist on Hub
Annotations must use the objects column with bbox, category (and optionally area) sub-fields
Bboxes can be in xywh (COCO) or xyxy (Pascal VOC) format — auto-detected and converted
Categories can be integers or strings — strings are auto-remapped to integer IDs
image_id column is optional — generated automatically if missing
ALWAYS validate unknown datasets before GPU training (see Dataset Validation section)

Read the full file on GitHub · 640 lines

Files

What ships with it

13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

yesterday Changed 22d97d6eba85
3d ago Changed 17f535c7a006
5d ago First seen · 640 lines · 194 tokens per session scan A ba79889361f6

Subscribe to this mod's changes

huggingface-vision-trainer is a skill published in the GitHub repository PracticalSwan/agent-skills (13 stars, last pushed yesterday), licensed MIT. It adds 194 tokens to every session and 8,049 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to huggingface-vision-trainer, differing in 60 lines, and is treated as a copy.

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