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Cataloged from google/tipsv2-b14-dpt
All models by google →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).
[0, 1] range, any resolution (multiples of 14 recommended)Apache 2.0
pip install transformers torch torchvision sentencepiecefrom transformers import AutoModelfrom torchvision import transformsfrom PIL import Imageimport requests
model = AutoModel.from_pretrained("google/tipsv2-b14-dpt", trust_remote_code=True)model.eval().cuda()
url = "https://huggingface.co/spaces/google/tipsv2/resolve/main/examples/depth/ade20k_00014.png"image = Image.open(requests.get(url, stream=True).raw)transform = transforms.Compose([transforms.Resize((448, 448)), transforms.ToTensor()])pixel_values = transform(image).unsqueeze(0).cuda()
# All tasks at onceoutputs = model(pixel_values)print(outputs.depth.shape) # (1, 1, 448, 448) — depth mapprint(outputs.normals.shape) # (1, 3, 448, 448) — surface normalsprint(outputs.segmentation.shape) # (1, 150, 448, 448) — segmentation logits
# Or individual tasks (only runs the requested head)depth = model.predict_depth(pixel_values)normals = model.predict_normals(pixel_values)seg = model.predict_segmentation(pixel_values)print(seg.argmax(dim=1).shape) # (1, 448, 448) — per-pixel class prediction@inproceedings{cao2026tipsv2, title = {{TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment}}, author = {Cao, Bingyi and Chen, Koert and Maninis, Kevis-Kokitsi and Chen, Kaifeng and Karpur, Arjun and Xia, Ye and Dua, Sahil and Dabral, Tanmaya and Han, Guangxing and Han, Bohyung and Ainslie, Joshua and Bewley, Alex and Jacob, Mithun and Wagner, Rene and Ramos, Washington and Choromanski, Krzysztof and Seyedhosseini, Mojtaba and Zhou, Howard and Araujo, Andre}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2026}, url = {https://arxiv.org/abs/2604.12012}}