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
| from torch import nn | |
| class MLP(nn.Module): | |
| def __init__(self, in_channels, hidden_size=256, projection_size=4096): | |
| super(MLP, self).__init__() | |
| self.net = nn.Sequential( | |
| nn.Linear(in_channels, hidden_size), | |
| nn.BatchNorm1d(hidden_size), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(hidden_size, projection_size), | |
| ) | |
| def forward(self, x): | |
| return self.net(x) | |