Model overview
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 SO400m variant with 412M vision params and 448M 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). - **Architecture**: ViT vision encoder (27 layers) + Transformer text encoder (27 layers) - **Image preprocessing**: resize to any resolution, convert to `[0, 1]` (no ImageNet normalization) - **Text preprocessing**: SentencePiece tokenizer, lowercased, max 64 tokens - **Patch size**: 14x14 pixels
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。