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, 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.
Pinned to revision 4e8964e24a5a, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/huggingface-vision-trainer/SKILL.md
- skills/huggingface-vision-trainer/LICENSE
- skills/huggingface-vision-trainer/references/finetune_sam2_trainer.md
- skills/huggingface-vision-trainer/references/hub_saving.md
- skills/huggingface-vision-trainer/references/image_classification_training_notebook.md
- skills/huggingface-vision-trainer/references/object_detection_training_notebook.md
- skills/huggingface-vision-trainer/references/reliability_principles.md
- skills/huggingface-vision-trainer/references/timm_trainer.md
- skills/huggingface-vision-trainer/scripts/dataset_inspector.py
- skills/huggingface-vision-trainer/scripts/estimate_cost.py
- skills/huggingface-vision-trainer/scripts/image_classification_training.py
- skills/huggingface-vision-trainer/scripts/object_detection_training.py
- skills/huggingface-vision-trainer/scripts/sam_segmentation_training.py
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