DPT
Dense Prediction Transformer
**Dense Prediction Transformers** (DPT) are a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) for dense prediction tasks.

The input image is transformed into tokens (orange) either by extracting non-overlapping patches followed by a linear projection of their flattened representation (DPT-Base and DPT-Large) or by applying a [ResNet](https://paperswithcode.com/method/resnet)-50 feature extractor (DPT-Hybrid). The image embedding is augmented with a positional embedding and a patch-independent readout token (red) is added. The tokens are passed through multiple [transformer](https://paperswithcode.com/method/transformer) stages. The tokens are reassembled from different stages into an image-like representation at multiple resolutions (green). Fusion modules (purple) progressively fuse and upsample the representations to generate a fine-grained prediction.
Vision Transformers for Dense Prediction
https://paperswithcode.com/paper/vision-transformers-for-dense-prediction
Vision Transformers for Dense Prediction