Instructions to use hf-tiny-model-private/tiny-random-DPTForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DPTForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-tiny-model-private/tiny-random-DPTForDepthEstimation")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DPTForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-tiny-model-private/tiny-random-DPTForDepthEstimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-DPTForDepthEstimation: direct link, hf CLI and curl.
- Browser
- Download file 76.3 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-DPTForDepthEstimation/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-DPTForDepthEstimation@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-DPTForDepthEstimation/resolve/refs%2Fpr%2F1/model.safetensors
76.3 MB
- Xet hash:
- f3c4e19e6903818efee3207b5df08832a3e4185dbb582a941ea0cc56469d9535
- Size of remote file:
- 76.3 MB
- SHA256:
- 909cd9ae7968b0f2f96daff2b9de1aadafd32d4d557ee8fe1f325eec394fa45a
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