| license: apache-2.0 | |
| tags: | |
| - vision | |
| - image-segmentation | |
| datasets: | |
| - scene_parse_150 | |
| widget: | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg | |
| example_title: Tiger | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg | |
| example_title: Teapot | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg | |
| example_title: Palace | |
| # DPT (large-sized model) fine-tuned on ADE20k | |
| The model is used for semantic segmentation of input images such as seen in the table below: | |
| | Input Image | Output Segmented Image | | |
| | --- | --- | | |
| |  | | | |
| ## Model description | |
| The Midas 3.0 nbased Dense Prediction Transformer (DPT) model was trained on ADE20k for semantic segmentation. It was introduced in the paper | |
| [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl-org/DPT). | |
| The MiDaS v3.0 DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for semantic segmentation. | |
|  | |
| Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face and the Intel AI Community team. | |
| ## Results: | |
| According to the authors, at the time of publication, when applied to semantic segmentation, dense vision transformers set a new state of the art on | |
| **ADE20K with 49.02% mIoU.** | |
| We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at | |
| [Intel DPT GItHub Repository](https://github.com/intel-isl/DPT). | |
| ## Intended uses & limitations | |
| You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=dpt) to look for | |
| fine-tuned versions on a task that interests you. | |
| ### How to use | |
| Here is how to use this model: | |
| ```python | |
| from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation | |
| from PIL import Image | |
| import requests | |
| url = "http://images.cocodataset.org/val2017/000000026204.jpg" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| feature_extractor = DPTImageProcessor .from_pretrained("Intel/dpt-large-ade") | |
| model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade") | |
| inputs = feature_extractor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| print(logits.shape) | |
| logits | |
| prediction = torch.nn.functional.interpolate( | |
| logits, | |
| size=image.size[::-1], # Reverse the size of the original image (width, height) | |
| mode="bicubic", | |
| align_corners=False | |
| ) | |
| # Convert logits to class predictions | |
| prediction = torch.argmax(prediction, dim=1) + 1 | |
| # Squeeze the prediction tensor to remove dimensions | |
| prediction = prediction.squeeze() | |
| # Move the prediction tensor to the CPU and convert it to a numpy array | |
| prediction = prediction.cpu().numpy() | |
| # Convert the prediction array to an image | |
| predicted_seg = Image.fromarray(prediction.squeeze().astype('uint8')) | |
| # Define the ADE20K palette | |
| adepallete = [0,0,0,120,120,120,180,120,120,6,230,230,80,50,50,4,200,3,120,120,80,140,140,140,204,5,255,230,230,230,4,250,7,224,5,255,235,255,7,150,5,61,120,120,70,8,255,51,255,6,82,143,255,140,204,255,4,255,51,7,204,70,3,0,102,200,61,230,250,255,6,51,11,102,255,255,7,71,255,9,224,9,7,230,220,220,220,255,9,92,112,9,255,8,255,214,7,255,224,255,184,6,10,255,71,255,41,10,7,255,255,224,255,8,102,8,255,255,61,6,255,194,7,255,122,8,0,255,20,255,8,41,255,5,153,6,51,255,235,12,255,160,150,20,0,163,255,140,140,140,250,10,15,20,255,0,31,255,0,255,31,0,255,224,0,153,255,0,0,0,255,255,71,0,0,235,255,0,173,255,31,0,255,11,200,200,255,82,0,0,255,245,0,61,255,0,255,112,0,255,133,255,0,0,255,163,0,255,102,0,194,255,0,0,143,255,51,255,0,0,82,255,0,255,41,0,255,173,10,0,255,173,255,0,0,255,153,255,92,0,255,0,255,255,0,245,255,0,102,255,173,0,255,0,20,255,184,184,0,31,255,0,255,61,0,71,255,255,0,204,0,255,194,0,255,82,0,10,255,0,112,255,51,0,255,0,194,255,0,122,255,0,255,163,255,153,0,0,255,10,255,112,0,143,255,0,82,0,255,163,255,0,255,235,0,8,184,170,133,0,255,0,255,92,184,0,255,255,0,31,0,184,255,0,214,255,255,0,112,92,255,0,0,224,255,112,224,255,70,184,160,163,0,255,153,0,255,71,255,0,255,0,163,255,204,0,255,0,143,0,255,235,133,255,0,255,0,235,245,0,255,255,0,122,255,245,0,10,190,212,214,255,0,0,204,255,20,0,255,255,255,0,0,153,255,0,41,255,0,255,204,41,0,255,41,255,0,173,0,255,0,245,255,71,0,255,122,0,255,0,255,184,0,92,255,184,255,0,0,133,255,255,214,0,25,194,194,102,255,0,92,0,255] | |
| # Apply the color map to the predicted segmentation image | |
| predicted_seg.putpalette(adepallete) | |
| # Blend the original image and the predicted segmentation image | |
| out = Image.blend(image, predicted_seg.convert("RGB"), alpha=0.5) | |
| out | |
| ``` | |
| For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt). | |
| ### BibTeX entry and citation info | |
| ```bibtex | |
| @article{DBLP:journals/corr/abs-2103-13413, | |
| author = {Ren{\'{e}} Ranftl and | |
| Alexey Bochkovskiy and | |
| Vladlen Koltun}, | |
| title = {Vision Transformers for Dense Prediction}, | |
| journal = {CoRR}, | |
| volume = {abs/2103.13413}, | |
| year = {2021}, | |
| url = {https://arxiv.org/abs/2103.13413}, | |
| eprinttype = {arXiv}, | |
| eprint = {2103.13413}, | |
| timestamp = {Wed, 07 Apr 2021 15:31:46 +0200}, | |
| biburl = {https://dblp.org/rec/journals/corr/abs-2103-13413.bib}, | |
| bibsource = {dblp computer science bibliography, https://dblp.org} | |
| } | |
| ``` |