huggingface-vision-trainer

by huggingface

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet,… npx skills add https://github.com/huggingface/skills --skill huggingface-vision-trainer Download ZIPGitHub10.8k

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

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

• ✗ NEEDS FORMATTING — Needs preprocessing (mapping code provided in output)

Automatic Bbox Preprocessing

The object detection training script (scripts/object_detection_training.py) automatically handles bbox format detection (xyxy→xywh conversion), bbox sanitization, image_id generation, string category→integer remapping, and dataset truncation. No manual preprocessing needed — just ensure the dataset has objects.bbox and objects.category columns.

Training workflow

Copy this checklist and track progress:

Training Progress:
- [ ] Step 1: Verify prerequisites (account, token, dataset)
- [ ] Step 2: Validate dataset format (run dataset_inspector.py)
- [ ] Step 3: Ask user about dataset size and validation split
- [ ] Step 4: Prepare training script (OD: scripts/object_detection_training.py, IC: scripts/image_classification_training.py, SAM: scripts/sam_segmentation_training.py)
- [ ] Step 5: Save script locally, submit job, and report details

Step 1: Verify prerequisites

Follow the Prerequisites Checklist above.

Step 2: Validate dataset

Run the dataset inspector BEFORE spending GPU time. See "Dataset Validation" section above.

Step 3: Ask user preferences

ALWAYS use the AskUserQuestion tool with option-style format:

AskUserQuestion({
 "questions": [
 {
 "question": "Do you want to run a quick test with a subset of the data first?",
 "header": "Dataset Size",
 "options": [
 {"label": "Quick test run (10% of data)", "description": "Faster, cheaper (~30-60 min, ~$2-5) to validate setup"},
 {"label": "Full dataset (Recommended)", "description": "Complete training for best model quality"}
 ],
 "multiSelect": false
 },
 {
 "question": "Do you want to create a validation split from the training data?",
 "header": "Split data",
 "options": [
 {"label": "Yes (Recommended)", "description": "Automatically split 15% of training data for validation"},
 {"label": "No", "description": "Use existing validation split from dataset"}
 ],
 "multiSelect": false
 },
 {
 "question": "Which GPU hardware do you want to use?",
 "header": "Hardware Flavor",
 "options": [
 {"label": "t4-small ($0.40/hr)", "description": "1x T4, 16 GB VRAM — sufficient for all OD models under 100M params"},
 {"label": "l4x1 ($0.80/hr)", "description": "1x L4, 24 GB VRAM — more headroom for large images or batch sizes"},
 {"label": "a10g-large ($1.50/hr)", "description": "1x A10G, 24 GB VRAM — faster training, more CPU/RAM"},
 {"label": "a100-large ($2.50/hr)", "description": "1x A100, 80 GB VRAM — fastest, for very large datasets or image sizes"}
 ],
 "multiSelect": false
 }
 ]
})

Step 4: Prepare training script

For object detection, use scripts/object_detection_training.py as the production-ready template. For image classification, use scripts/image_classification_training.py. For SAM/SAM2 segmentation, use scripts/sam_segmentation_training.py. All scripts use HfArgumentParser — all configuration is passed via CLI arguments in script_args, NOT by editing Python variables. For timm model details, see references/timm_trainer.md. For SAM2 training details, see references/finetune_sam2_trainer.md.

Step 5: Save script, submit job, and report

• Save the script locally to submitted_jobs/ in the workspace root (create if needed) with a descriptive name like training_<dataset>_<YYYYMMDD_HHMMSS>.py. Tell the user the path.

• Submit using hf_jobs MCP tool (preferred) or HfApi().run_uv_job() — see directive #1 for both methods. Pass all config via script_args.

• Report the job ID (from .id attribute), monitoring URL, Trackio dashboard (https://huggingface.co/spaces/{username}/trackio), expected time, and estimated cost.

• Wait for user to request status checks — don't poll automatically. Training jobs run asynchronously and can take hours.

Critical directives

These rules prevent common failures. Follow them exactly.

1. Job submission: hf_jobs MCP tool vs Python API

hf_jobs() is an MCP tool, NOT a Python function. Do NOT try to import it from huggingface_hub. Call it as a tool:

hf_jobs("uv", {"script": training_script_content, "flavor": "a10g-large", "timeout": "4h", "secrets": {"HF_TOKEN": "$HF_TOKEN"}})

If hf_jobs MCP tool is unavailable, use the Python API directly:

from huggingface_hub import HfApi, get_token
api = HfApi()
job_info = api.run_uv_job(
 script="path/to/training_script.py", # file PATH, NOT content
 script_args=["--dataset_name", "cppe-5", ...],
 flavor="a10g-large",
 timeout=14400, # seconds (4 hours)
 env={"PYTHONUNBUFFERED": "1"},
 secrets={"HF_TOKEN": get_token()}, # MUST use get_token(), NOT "$HF_TOKEN"
)
print(f"Job ID: {job_info.id}")

Critical differences between the two methods:

hf_jobs MCP tool HfApi().run_uv_job() script param Python code string or URL (NOT local paths) File path to .py file (NOT content) Token in secrets "$HF_TOKEN" (auto-replaced) get_token() (actual token value) Timeout format String ("4h") Seconds (14400)

Rules for both methods:

• The training script MUST include PEP 723 inline metadata with dependencies

• Do NOT use image or command parameters (those belong to run_job(), not run_uv_job())

2. Authentication via job secrets + explicit hub_token injection

Job config MUST include the token in secrets — syntax depends on submission method (see table above).

Training script requirement: The Transformers Trainer calls create_repo(token=self.args.hub_token) during __init__() when push_to_hub=True. The training script MUST inject HF_TOKEN into training_args.hub_token AFTER parsing args but BEFORE creating the Trainer. The template scripts/object_detection_training.py already includes this:

hf_token = os.environ.get("HF_TOKEN")
if training_args.push_to_hub and not training_args.hub_token:
 if hf_token:
 training_args.hub_token = hf_token

If you write a custom script, you MUST include this token injection before the Trainer(...) call.

• Do NOT call login() in custom scripts unless replicating the full pattern from scripts/object_detection_training.py

• Do NOT rely on implicit token resolution (hub_token=None) — unreliable in Jobs

• See the hugging-face-jobs skill → Token Usage Guide for full details

3. JobInfo attribute

Access the job identifier using .id (NOT .job_id or .name — these don't exist):

job_info = api.run_uv_job(...) # or hf_jobs("uv", {...})
job_id = job_info.id # Correct -- returns string like "687fb701029421ae5549d998"

4. Required training flags and HfArgumentParser boolean syntax

scripts/object_detection_training.py uses HfArgumentParser — all config is passed via script_args. Boolean arguments have two syntaxes:

• bool fields (e.g., push_to_hub, do_train): Use as bare flags (--push_to_hub) or negate with --no_ prefix (--no_remove_unused_columns)

• Optional[bool] fields (e.g., greater_is_better): MUST pass explicit value (--greater_is_better True). Bare --greater_is_better causes error: expected one argument

Required flags for object detection:

--no_remove_unused_columns # MUST: preserves image column for pixel_values
--no_eval_do_concat_batches # MUST: images have different numbers of target boxes
--push_to_hub # MUST: environment is ephemeral
--hub_model_id username/model-name
--metric_for_best_model eval_map
--greater_is_better True # MUST pass "True" explicitly (Optional[bool])
--do_train
--do_eval

Required flags for image classification:

--no_remove_unused_columns # MUST: preserves image column for pixel_values
--push_to_hub # MUST: environment is ephemeral
--hub_model_id username/model-name
--metric_for_best_model eval_accuracy
--greater_is_better True # MUST pass "True" explicitly (Optional[bool])
--do_train
--do_eval

Required flags for SAM/SAM2 segmentation:

--remove_unused_columns False # MUST: preserves input_boxes/input_points
--push_to_hub # MUST: environment is ephemeral
--hub_model_id username/model-name
--do_train
--prompt_type bbox # or "point"
--dataloader_pin_memory False # MUST: avoids pin_memory issues with custom collator

5. Timeout management

Default 30 min is TOO SHORT for object detection. Set minimum 2-4 hours. Add 30% buffer for model loading, preprocessing, and Hub push.

Scenario Timeout Quick test (100-200 images, 5-10 epochs) 1h Development (500-1K images, 15-20 epochs) 2-3h Production (1K-5K images, 30 epochs) 4-6h Large dataset (5K+ images) 6-12h

6. Trackio monitoring

Trackio is always enabled in the object detection training script — it calls trackio.init() and trackio.finish() automatically. No need to pass --report_to trackio. The project name is taken from --output_dir and the run name from --run_name. For image classification, pass --report_to trackio in TrainingArguments.

Dashboard at: https://huggingface.co/spaces/{username}/trackio

Model & hardware selection

Recommended object detection models

Model Params Use case ustc-community/dfine-small-coco 10.4M Best starting point — fast, cheap, SOTA quality PekingU/rtdetr_v2_r18vd 20.2M Lightweight real-time detector ustc-community/dfine-large-coco 31.4M Higher accuracy, still efficient PekingU/rtdetr_v2_r50vd 43M Strong real-time baseline ustc-community/dfine-xlarge-obj365 63.5M Best accuracy (pretrained on Objects365) PekingU/rtdetr_v2_r101vd 76M Largest RT-DETR v2 variant

Start with ustc-community/dfine-small-coco for fast iteration. Move to D-FINE Large or RT-DETR v2 R50 for better accuracy.

Recommended image classification models

All timm/ models work out of the box via AutoModelForImageClassification (loaded as TimmWrapperForImageClassification). See references/timm_trainer.md for details.

Model Params Use case timm/mobilenetv3_small_100.lamb_in1k 2.5M Ultra-lightweight — mobile/edge, fastest training timm/mobilevit_s.cvnets_in1k 5.6M Mobile transformer — good accuracy/speed trade-off timm/resnet50.a1_in1k 25.6M Strong CNN baseline — reliable, well-studied timm/vit_base_patch16_dinov3.lvd1689m 86.6M Best accuracy — DINOv3 self-supervised ViT

Start with timm/mobilenetv3_small_100.lamb_in1k for fast iteration. Move to timm/resnet50.a1_in1k or timm/vit_base_patch16_dinov3.lvd1689m for better accuracy.

Recommended SAM/SAM2 segmentation models

Model Params Use case facebook/sam2.1-hiera-tiny 38.9M Fastest SAM2 — good for quick experiments facebook/sam2.1-hiera-small 46.0M Best starting point — good quality/speed balance facebook/sam2.1-hiera-base-plus 80.8M Higher capacity for complex segmentation facebook/sam2.1-hiera-large 224.4M Best SAM2 accuracy — requires more VRAM facebook/sam-vit-base 93.7M Original SAM — ViT-B backbone facebook/sam-vit-large 312.3M Original SAM — ViT-L backbone facebook/sam-vit-huge 641.1M Original SAM — ViT-H, best SAM v1 accuracy

Start with facebook/sam2.1-hiera-small for fast iteration. SAM2 models are generally more efficient than SAM v1 at similar quality. Only the mask decoder is trained by default (vision and prompt encoders are frozen).

Hardware recommendation

All recommended OD and IC models are under 100M params — t4-small (16 GB VRAM, $0.40/hr) is sufficient for all of them. Image classification models are generally smaller and faster than object detection models — t4-small handles even ViT-Base comfortably. For SAM2 models up to hiera-base-plus, t4-small is sufficient since only the mask decoder is trained. For sam2.1-hiera-large or SAM v1 models, use l4x1 or a10g-large. Only upgrade if you hit OOM from large batch sizes — reduce batch size first before switching hardware. Common upgrade path: t4-small → l4x1 ($0.80/hr, 24 GB) → a10g-large ($1.50/hr, 24 GB).

For full hardware flavor list: refer to the hugging-face-jobs skill. For cost estimation: run scripts/estimate_cost.py.

Checking job status

MCP tool (if available):

hf_jobs("ps") # List all jobs
hf_jobs("logs", {"job_id": "your-job-id"}) # View logs
hf_jobs("inspect", {"job_id": "your-job-id"}) # Job details

Python API fallback:

from huggingface_hub import HfApi
api = HfApi()
api.list_jobs() # List all jobs
api.get_job_logs(job_id="your-job-id") # View logs
api.get_job(job_id="your-job-id") # Job details

Common failure modes

OOM (CUDA out of memory)

Reduce per_device_train_batch_size (try 4, then 2), reduce IMAGE_SIZE, or upgrade hardware.

Dataset format errors

Run scripts/dataset_inspector.py first. The training script auto-detects xyxy vs xywh, converts string categories to integer IDs, and adds image_id if missing. Ensure objects.bbox contains 4-value coordinate lists in absolute pixels and objects.category contains either integer IDs or string labels.

Hub push failures (401)

Verify: (1) job secrets include token (see directive #2), (2) script sets training_args.hub_token BEFORE creating the Trainer, (3) push_to_hub=True is set, (4) correct hub_model_id, (5) token has write permissions.

Job timeout

Increase timeout (see directive #5 table), reduce epochs/dataset, or use checkpoint strategy with hub_strategy="every_save".

KeyError: 'test' (missing test split)

The object detection training script handles this gracefully — it falls back to the validation split. Ensure you're using the latest scripts/object_detection_training.py.

Single-class dataset: "iteration over a 0-d tensor"

torchmetrics.MeanAveragePrecision returns scalar (0-d) tensors for per-class metrics when there's only one class. The template scripts/object_detection_training.py handles this by calling .unsqueeze(0) on these tensors. Ensure you're using the latest template.

Poor detection performance (mAP < 0.15)

Increase epochs (30-50), ensure 500+ images, check per-class mAP for imbalanced classes, try different learning rates (1e-5 to 1e-4), increase image size.

For comprehensive troubleshooting: see references/reliability_principles.md

Reference files

• scripts/object_detection_training.py — Production-ready object detection training script

• scripts/image_classification_training.py — Production-ready image classification training script (supports timm models)

• scripts/sam_segmentation_training.py — Production-ready SAM/SAM2 segmentation training script (bbox & point prompts)

• scripts/dataset_inspector.py — Validate dataset format for OD, classification, and SAM segmentation

• scripts/estimate_cost.py — Estimate training costs for any vision model (includes SAM/SAM2)

• references/object_detection_training_notebook.md — Object detection training workflow, augmentation strategies, and training patterns

• references/image_classification_training_notebook.md — Image classification training workflow with ViT, preprocessing, and evaluation

• references/finetune_sam2_trainer.md — SAM2 fine-tuning walkthrough with MicroMat dataset, DiceCE loss, and Trainer integration

• references/timm_trainer.md — Using timm models with HF Trainer (TimmWrapper, transforms, full example)

• references/hub_saving.md — Detailed Hub persistence guide and verification checklist

• references/reliability_principles.md — Failure prevention principles from production experience

External links

• Transformers Object Detection Guide

• Transformers Image Classification Guide

• DETR Model Documentation

• ViT Model Documentation

• HF Jobs Guide — Main Jobs documentation

• HF Jobs Configuration — Hardware, secrets, timeouts, namespaces

• HF Jobs CLI Reference — Command line interface

• Object Detection Models

• Image Classification Models

• SAM2 Model Documentation

• SAM Model Documentation

• Object Detection Datasets

• Image Classification Datasets