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hf-tiny-model-private
/
tiny-random-DPTForDepthEstimation

Depth Estimation
Transformers
PyTorch
dpt
Model card Files Files and versions
xet
Community
1

Instructions to use hf-tiny-model-private/tiny-random-DPTForDepthEstimation 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-DPTForDepthEstimation 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-DPTForDepthEstimation")
    # Load model directly
    from transformers import AutoImageProcessor, AutoModelForDepthEstimation
    
    processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DPTForDepthEstimation")
    model = AutoModelForDepthEstimation.from_pretrained("hf-tiny-model-private/tiny-random-DPTForDepthEstimation")
  • Notebooks
  • Google Colab
  • Kaggle
tiny-random-DPTForDepthEstimation
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  • 1 contributor
History: 3 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
e5b5ba5 verified over 1 year ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • config.json
    1.06 kB
    Upload tiny models for DPTForDepthEstimation about 3 years ago
  • model.safetensors
    76.3 MB
    xet
    Adding `safetensors` variant of this model over 1 year ago
  • preprocessor_config.json
    399 Bytes
    Upload tiny models for DPTForDepthEstimation about 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    76.3 MB
    xet
    Upload tiny models for DPTForDepthEstimation about 3 years ago