

 DPT architecture. Taken from the original paper. 
This model was contributed by nielsr. The original code can be found here.
Usage tips
DPT is compatible with the [AutoBackbone] class. This allows to use the DPT framework with various computer vision backbones available in the library, such as [VitDetBackbone] or [Dinov2Backbone]. One can create it as follows:
thon
from transformers import Dinov2Config, DPTConfig, DPTForDepthEstimation
initialize with a Transformer-based backbone such as DINOv2
in that case, we also specify reshape_hidden_states=False to get feature maps of shape (batch_size, num_channels, height, width)
backbone_config = Dinov2Config.from_pretrained("facebook/dinov2-base", out_features=["stage1", "stage2", "stage3", "stage4"], reshape_hidden_states=False)
config = DPTConfig(backbone_config=backbone_config)
model = DPTForDepthEstimation(config=config)