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")
# pip install -U transformers accelerate # 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")
https://huggingface.co/hf-tiny-model-private/tiny-random-GLPNForDepthEstimation/resolve/refs%2Fpr%2F1/README.md
hf download hf://hf-tiny-model-private/tiny-random-GLPNForDepthEstimation@refs/pr/1/README.md
curl -L -o README.md https://huggingface.co/hf-tiny-model-private/tiny-random-GLPNForDepthEstimation/resolve/refs%2Fpr%2F1/README.md
license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts language: - en metrics: - bleu library_name: adapter-transformers pipeline_tag: image-to-image
