tipsv2-b14

pip install transformers torch torchvision sentencepiece scikit-learn

from transformers import AutoModel

model = AutoModel.from_pretrained("google/tipsv2-b14", trust_remote_code=True)
model.eval()

from torchvision import transforms
from PIL import Image
import 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 embedding
print(out.patch_tokens.shape)  # (1, 1024, 768) — per-patch spatial features

text_emb = model.encode_text(["a photo of a bus", "a photo of a dog"])
print(text_emb.shape)  # (2, 768) — one embedding per query

import 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.T
print(classes[similarity.argmax()])  # bus — predicted class

import numpy as np
from 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 contrast
print(rgb.shape)  # (32, 32, 3) — PCA of patch features as RGB

model = 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}
}