Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →sediman-agent/huggingface-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 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, Tr
Passed 16 of 19 test cases with the skill, and 8 of 19 without it.
and 7 more in the full report
10 fixed9 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When evaluating D-FINE on object detection datasets where images contain varying numbers of bounding boxes, Trainer throws shape mismatch errors during batch concating. Which exact command-line argument prevents Trainer from concatenating evaluation batches?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies the argument '--no_eval_do_concat_batches'. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
Use this skill when users want to:
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.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
Before starting any training job, verify:
hf_whoami() (tool) or hf auth whoami (terminal)test cases passed, vs 8 / 19 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.