Instructions to use hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0c81e9d985d0a89deedcc1dc073a84e38d0ae6ed4dca8aacf980028320ee8f5
- Size of remote file:
- 39.5 kB
- SHA256:
- dbd5d6b6138df5f522864d31104616ab55771e602f54d72d721ad3676c4803d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.