hugging-face-vision-trainer

Solid

Train or fine-tune vision models on Hugging Face Jobs for detection, classification, and SAM or SAM2 segmentation.

AI & Automation 39,350 stars 6386 forks Updated today MIT

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Skill Content

# 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 ...

Details

Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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