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Cataloged from google/tipsv2-b14
All models by google →TIPSv2 (Text-Image Pre-training with Spatial awareness) is a family of contrastive vision-language models that produce spatially rich image features aligned with text embeddings. This is the Base variant with 86M vision params and 110M text params. Try the code snippets below or check out the GitHub repo for more use cases and visualizations, including zero-shot segmentation.
Images should be tensors in [0, 1] range (just ToTensor(), no ImageNet normalization).
[0, 1] (no ImageNet normalization)Apache 2.0
pip install transformers torch torchvision sentencepiece scikit-learnfrom transformers import AutoModel
model = AutoModel.from_pretrained("google/tipsv2-b14", trust_remote_code=True)model.eval()from torchvision import transformsfrom PIL import Imageimport requests
transform = transforms.Compose([ transforms.Resize((448, 448)), transforms.ToTensor(),])
url = "https://huggingface.co/spaces/google/TIPSv2/resolve/main/examples/zeroseg/pascal_context_00049_image.png"image = Image.open(requests.get(url, stream=True).raw)pixel_values = transform(image).unsqueeze(0)out = model.encode_image(pixel_values)
print(out.cls_token.shape) # (1, 1, 768) — global image embeddingprint(out.patch_tokens.shape) # (1, 1024, 768) — per-patch spatial featurestext_emb = model.encode_text(["a photo of a bus", "a photo of a dog"])print(text_emb.shape) # (2, 768) — one embedding per queryimport torch.nn.functional as F
classes = ["bus", "car", "dog", "cat"]cls = F.normalize(out.cls_token[:, 0, :], dim=-1)text_emb = F.normalize(model.encode_text(classes), dim=-1)similarity = cls @ text_emb.Tprint(classes[similarity.argmax()]) # bus — predicted classimport numpy as npfrom sklearn.decomposition import PCA
spatial = out.patch_tokens.reshape(1, 32, 32, 768)feat = spatial[0].detach().cpu().numpy().reshape(-1, 768)rgb = PCA(n_components=3, whiten=True).fit_transform(feat).reshape(32, 32, 3)rgb = 1 / (1 + np.exp(-2.0 * rgb)) # sigmoid for [0, 1] range with good contrastprint(rgb.shape) # (32, 32, 3) — PCA of patch features as RGBmodel = model.cuda()out = model.encode_image(pixel_values.cuda())text_emb = model.encode_text(["a city"])@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}}