skills.rest
tripplen23
tripplen23COMMUNITY
Explore all skills by this author →
WebsiteGitHub

huggingface-vision-trainer

Fine-tune vision models on Hugging Face Jobs with COCO datasets.

1|Updated Feb 15, 2026
Search Tags:#MLOps#fine-tuning#cloud#vision#training#huggingface#hub
One-click install
npx skills add https://github.com/tripplen23/finetuning-sessions --skill huggingface-vision-trainer-tripplen23
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-vision-trainer
Source: https://github.com/tripplen23/finetuning-sessions/tree/main/.kiro/skills/huggingface-vision-trainer
Command: npx skills add https://github.com/tripplen23/finetuning-sessions --skill huggingface-vision-trainer-tripplen23

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, evaluate, scikit-learn, torchvision, monai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Trains and fine-tunes vision models (object detection, image classification, SAM/SAM2) on Hugging Face Jobs cloud GPUs, eliminating local hardware obstacles and enabling Hub persistence.

Core Features & Use Cases

  • End-to-end vision training on HF Jobs for OD, IC, and SAM/SAM2.
  • Supports common models (DETR, RT-DETR, YOLOS, timm classifiers, SAM2) with COCO formatting, augmentation, and mAP/mAR evaluation.
  • Hub persistence: push trained models to Hugging Face Hub with token-based authentication and Trackio monitoring.

Quick Start

Submit a training job using the included scripts to fine-tune object detection, image classification, or SAM models on Hugging Face Jobs.

Frequently Asked Questions about huggingface-vision-trainer

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

FAQPage Schema
How do I fine-tune a Hugging Face vision model on cloud GPUs?▼

Fine-tuning vision models on Hugging Face Jobs uses included scripts to orchestrate end-to-end training for object detection, image classification, and SAM/SAM2 segmentation on cloud GPUs.

Can I train RT-DETR and YOLOS object detection models using COCO formatted datasets?▼

Yes, training RT-DETR and YOLOS object detection models supports COCO-formatted datasets, applying albumentations augmentation and evaluating results with torchmetrics mAP and mAR scores.

Does this workflow support pushing trained timm classifiers to the Hugging Face Hub?▼

Yes, pushing trained timm classifiers to the Hugging Face Hub is supported through token-based authentication, ensuring model persistence and experiment monitoring with Trackio.

What dependencies do I need to set up SAM2 segmentation pipelines on HF Jobs?▼

Setting up SAM2 segmentation pipelines requires dependencies including transformers, accelerate, datasets, timm, pycocotools, and monai to execute end-to-end training workflows on Hugging Face Jobs.

Are there limitations when running D-FINE and DETR training workflows without local hardware?▼

Running D-FINE and DETR training workflows without local hardware has no limitations here; Hugging Face Jobs cloud GPUs handle the compute entirely, ensuring repeatable and auditable experiments.

Related Skills

Explore More Software Engineering Skills→
tony-ng-vntony-ng-vn

huggingface-jobs

Run Python workloads on Hugging Face Jobs with GPU/CPU resources.

Community
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
evalstateevalstate

trl

Submit TRL training jobs on Hugging Face Jobs and convert models to GGUF.

Community
Advanced
NagyViktNagyVikt
2

huggingface-vision-trainer

Fine-tune computer vision models on Hugging Face Jobs infrastructure.

Community
Advanced