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DPT (Dense Prediction Transformer) heads for depth estimation, surface normal prediction, and semantic segmentation on top of the frozen TIPSv2 B/14 backbone. The backbone is loaded automatically. The depth and normals heads are trained on the NYU Depth V2 dataset and segmentation is trained on the ADE20K dataset (150 classes).

Depth estimation
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021) and first released in this repository. DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation. This repository hosts the "hybrid" version of the model as stated in the paper. DPT-Hybrid diverges from DPT by using ViT-hybrid as a backbone and taking some activations from the backbone. The model card has been written in combination by the Hugging Face team and Intel.

Depth estimation
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Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021) and first released in this repository. DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation. The model card has been written in combination by the Hugging Face team and Intel. In case you want to implement the entire logic yourself, here's how to do that for zero-shot depth estimation on an image:

Depth estimation
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ZoeDepth model fine-tuned on the NYU dataset. It was introduced in the paper ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth by Shariq et al. and first released in this repository. ZoeDepth extends the DPT framework for metric (also called absolute) depth estimation, obtaining state-of-the-art results. Disclaimer: The team releasing ZoeDepth did not write a model card for this model so this model card has been written by the Hugging Face team.

Depth estimation
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ZoeDepth model fine-tuned on the NYU and KITTI datasets. It was introduced in the paper ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth by Shariq et al. and first released in this repository. ZoeDepth extends the DPT framework for metric (also called absolute) depth estimation, obtaining state-of-the-art results. Disclaimer: The team releasing ZoeDepth did not write a model card for this model so this model card has been written by the Hugging Face team.

Depth estimation
Input Image→ Output Image
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