Instructions to use hf-internal-testing/tiny-random-DPTForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DPTForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-internal-testing/tiny-random-DPTForDepthEstimation")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DPTForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-internal-testing/tiny-random-DPTForDepthEstimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-DPTForDepthEstimation: direct link, hf CLI and curl.
- Browser
- Download file 76.3 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-DPTForDepthEstimation/resolve/36dfbfee8e1a860f08f19939f1a0ca091f0bd8eb/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-DPTForDepthEstimation@36dfbfee8e1a860f08f19939f1a0ca091f0bd8eb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-DPTForDepthEstimation/resolve/36dfbfee8e1a860f08f19939f1a0ca091f0bd8eb/pytorch_model.bin
76.3 MB
- Xet hash:
- 8a1b32de6033ac23415150c3c04a8538eb7817cf6029624856db083a42bde6f8
- Size of remote file:
- 76.3 MB
- SHA256:
- 746a123575ccaf12c050845f42b2a76b1fdac47ab1d1c0da6afa4573dab0916a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.