/huggingface-vision-trainer

name: huggingface-vision-trainer
description: 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 cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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`:

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](https://hf.co/pro), [Team](https://hf.co/enterprise), or [Enterprise](https://hf.co/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)

Dataset Requirements — Image Classification

• Dataset must exist on Hub
• Must have an **`image` column** (PIL images) and a **`label` column** (integer class IDs or strings)
• The label column can be `ClassLabel` type (with names) or plain integers/strings — strings are auto-remapped
• Common column names auto-detected: `label`, `labels`, `class`, `fine_label`
• **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)

Dataset Requirements — SAM/SAM2 Segmentation

• Dataset must exist on Hub
• Must have an **`image` column** (PIL images) and a **`mask` column** (binary ground-truth segmentation mask)
• Must have a **prompt** — either:
• A **`prompt` column** with JSON containing `{"bbox": [x0,y0,x1,y1]}` or `{"point": [x,y]}`
• OR a dedicated **`bbox`** column with `[x0,y0,x1,y1]` values
• OR a dedicated **`point`** column with `[x,y]` or `[[x,y],...]` values
• Bboxes should be in **xyxy** format (absolute pixel coordinates)
• Example dataset: `merve/MicroMat-mini` (image matting with bbox prompts)
• **ALWAYS validate unknown datasets** before GPU training (see Dataset Validation section)

Critical Settings

• **Timeout must exceed expected training time** — Default 30min is TOO SHORT. See directive #6 for recommended values.
• **Hub push must be enabled** — `push_to_hub=True`, `hub_model_id="username/model-name"`, token in `secrets`

Dataset Validation

**Validate dataset format BEFORE launching GPU training to prevent the #1 cause of training failures: format mismatches.**

**ALWAYS validate for** unknown/custom datasets or any dataset you haven't trained with before. **Skip for** `cppe-5` (the default in the training script).

Running the Inspector

**Option 1: Via HF Jobs (recommended — avoids local SSL/dependency issues):**

hf_jobs("uv", {
    "script": "path/to/dataset_inspector.py",
    "script_args": ["--dataset", "username/dataset-name", "--split", "train"]
})

**Option 2: Locally:**

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

**Option 3: Via `HfApi().run_uv_job()` (if hf_jobs MCP unavailable):**

from huggingface_hub import HfApi
api = HfApi()
api.run_uv_job(
    script="scripts/dataset_inspector.py",
    script_args=["--dataset", "username/dataset-name", "--split", "train"],
    flavor="cpu-basic",
    timeout=300,
)

Reading Results

• **`✓ READY`** — Dataset is compatible, use directly
• **`✗