huggingface-vision-trainer

To fine-tune a vision model on Hugging Face Jobs, you can use this skill to validate your dataset, estimate hardware costs, and launch automated training for object detection, classification, or segmentation tasks. It handles model persistence to the Hub automatically.

Yes, you can train a segmentation model using SAM2. This skill integrates Hugging Face Transformers and the monai library to execute training and evaluation for segmentation networks directly on cloud GPUs.

Yes, this training workflow supports custom datasets for object detection. It includes automated dataset validation to ensure your data is correctly formatted before launching the fine-tuning job on architectures like D-FINE or RT-DETR.

The best way to estimate GPU costs for fine-tuning computer vision models is using this skill's hardware-specific cost estimation feature. It calculates expected expenses before launching your training job on Hugging Face Jobs infrastructure.

You can monitor training jobs and save results to the Hugging Face Hub by utilizing the integrated Trackio monitoring and automated model persistence features. This ensures your fine-tuned checkpoints are safely stored after training completes.