Image Feature Extraction
Transformers
Safetensors
skinmap
feature-extraction
dermatology
medical-imaging
embeddings
clip
custom_code
Instructions to use Digital-Dermatology/SkinMap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Digital-Dermatology/SkinMap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Digital-Dermatology/SkinMap", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Digital-Dermatology/SkinMap", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| import sys | |
| from loguru import logger | |
| from ..utils.utils import is_main_process | |
| def set_log_level( | |
| min_log_level: str = "INFO", | |
| log_format: str = "<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | <level>{level: <8}</level> | <level>{message}</level>", | |
| ): | |
| def log_level_filter(record): | |
| return ( | |
| record["level"].no >= logger.level(min_log_level).no and is_main_process() | |
| ) | |
| logger.remove() | |
| logger.add(sys.stderr, filter=log_level_filter, format=log_format) | |