Depth Estimation
Transformers
Safetensors
tipsv2_dpt
feature-extraction
vision
surface-normals
semantic-segmentation
dense-prediction
custom_code
Instructions to use google/tipsv2-g14-dpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-g14-dpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="google/tipsv2-g14-dpt", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/tipsv2-g14-dpt", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Gabriele commited on
Commit ·
7eec622
1
Parent(s): 109ccc8
Update example image URL to use HF-hosted ADE20K image
Browse files
README.md
CHANGED
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@@ -36,7 +36,7 @@ import requests
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model = AutoModel.from_pretrained("google/tipsv2-g14-dpt", trust_remote_code=True)
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model.eval().cuda()
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url = "https://
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image = Image.open(requests.get(url, stream=True).raw)
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transform = transforms.Compose([transforms.Resize((448, 448)), transforms.ToTensor()])
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pixel_values = transform(image).unsqueeze(0).cuda()
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model = AutoModel.from_pretrained("google/tipsv2-g14-dpt", trust_remote_code=True)
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model.eval().cuda()
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url = "https://huggingface.co/spaces/google/tipsv2/resolve/main/examples/depth/ade20k_00014.png"
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image = Image.open(requests.get(url, stream=True).raw)
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transform = transforms.Compose([transforms.Resize((448, 448)), transforms.ToTensor()])
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pixel_values = transform(image).unsqueeze(0).cuda()
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