Glpn Nyu

--- license: apache-2.0 tags: - vision - depth-estimation 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 --- # GLPN fine-tuned on NYUv2 Global-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Kim et al. and first released in [this repository](https://github.com/vinvino02/GLPDepth). Disclaimer: The team releasing GLPN did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description GLPN uses SegFormer as backbone and adds a lightweight head on top for depth estimation.  ## Intended uses & limitations You can use the raw model for monocular depth estimation. See the [model hub](https://huggingface.co/models?search=glpn) 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 GLPNImageProcessor, GLPNForDepthEstimation import torch import numpy as np from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu") model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu") # prepare image for the model inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) # visualize the prediction output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs...