Instructions to use RGBD-SOD/dptdepth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RGBD-SOD/dptdepth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RGBD-SOD/dptdepth", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RGBD-SOD/dptdepth", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| import torch | |
| class BaseModel(torch.nn.Module): | |
| def load(self, path): | |
| """Load model from file. | |
| Args: | |
| path (str): file path | |
| """ | |
| parameters = torch.load(path, map_location=torch.device("cpu")) | |
| if "optimizer" in parameters: | |
| parameters = parameters["model"] | |
| self.load_state_dict(parameters) | |