hugging-face-vision-trainer
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs...
Profile →npx skills add sickn33/agentic-awesome-skills --skill hugging-face-vision-trainer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Trains and fine-tunes vision models for object detection, image classification, and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs, with results saved to the Hub.
How it works
The skill instructs the agent to set up cloud-based vision training jobs for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models and Transformers classifiers), and SAM/SAM2 segmentation. It requires authentication, dataset validation, and preparation of training scripts (object_detection_training.py, image_classification_training.py, sam_segmentation_training.py). Training runs via the hf_jobs MCP tool or via HfApi().run_uv_job(), with configuration passed through CLI arguments using HfArgumentParser. The process includes injecting HF_TOKEN into training_args.hub_token if push_to_hub is enabled, saving the training script under submitted_jobs/, submitting the job, and reporting the job ID and monitoring URL.
When to use it
Use this skill to fine-tune object detectors on cloud GPUs or local hardware, fine-tune image classifiers on cloud GPUs or local, or fine-tune SAM/SAM2 segmentation models using prompts. It is intended for users who want to run vision training jobs on Hugging Face Jobs and persist models to the Hub.
What it can touch
- Scripts: scripts/object_detection_training.py, scripts/image_classification_training.py, scripts/sam_segmentation_training.py
- Authentication: reads HF_TOKEN / hub_token handling
- Job submission: uses hf_jobs("uv", ...) or HfApi().run_uv_job()
- Output: results saved to Hugging Face Hub, and Trackio dashboard monitoring URL
Caveats
- Requires a paid Hugging Face plan for Jobs.
- Training scripts must include PEP 723 inline metadata with dependencies.
- Token handling must inject HF_TOKEN into training_args.hub_token when pushing to hub.
- Timeouts must be extended beyond default; guidelines specify 2-4 hours minimum depending on scenario.
- Trackio monitoring is enabled by default in the object detection workflow.
# 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 q
- When to Use This Skill
- Related Skills
- Local Script Execution
- Prerequisites Checklist
- Account & Authentication
- Dataset Requirements — Object Detection
- Dataset Requirements — Image Classification
- Dataset Requirements — SAM/SAM2 Segmentation
- Critical Settings
- Dataset Validation
- Running the Inspector
- Reading Results
- Automatic Bbox Preprocessing
- Training workflow
uv run scripts/dataset_inspector.py --dataset username/dataset-name --split train uv run scripts/estimate_cost.py --help
What does the hugging-face-vision-trainer skill do?
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs...
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-vision-trainer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.