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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hf-transformers-bot HF Staff
Update tiny models for DepthAnythingForDepthEstimation
b1d8a13 verified Download model.safetensors from hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation: direct link, hf CLI and curl.
- Browser
- Download file 39.5 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation/resolve/refs%2Fpr%2F3/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation@refs/pr/3/model.safetensors
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curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation/resolve/refs%2Fpr%2F3/model.safetensors
39.5 kB
- Xet hash:
- 36d211b7f56ac687c02a1f70cb4eee601bae029a2eb54f0a01e1881cd9bcd06c
- Size of remote file:
- 39.5 kB
- SHA256:
- b98e3e2c877c8439c9fd14d90a0375f1b8a99f1ae7d63ff124c3b7a10bdd6c05
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