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 os | |
| import yaml | |
| class Loader(yaml.SafeLoader): | |
| def __init__(self, stream): | |
| self._root = os.path.split(stream.name)[0] | |
| super(Loader, self).__init__(stream) | |
| def include(self, node): | |
| filename = os.path.join(self._root, self.construct_scalar(node)) | |
| with open(filename, "r") as f: | |
| return yaml.load(f, Loader) | |
| Loader.add_constructor("!include", Loader.include) | |
| def merge_dicts(a, b): | |
| """Recursively merge dict b into dict a.""" | |
| for key, value in b.items(): | |
| if key in a and isinstance(a[key], dict) and isinstance(value, dict): | |
| merge_dicts(a[key], value) | |
| else: | |
| a[key] = value | |
| return a | |