Instructions to use hf-tiny-model-private/tiny-random-GLPNForDepthEstimation 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-GLPNForDepthEstimation 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-GLPNForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-GLPNForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-tiny-model-private/tiny-random-GLPNForDepthEstimation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 162 Bytes
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license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- bleu
library_name: adapter-transformers
pipeline_tag: image-to-image
--- |