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bioclip-2

imageomics · bioclip-2

/resolve/main/ )| |:--| |**Figure #.** Image of .| --> BioCLIP 2 is a foundation model for biology organismal images. It is trained on TreeOfLife-200M on the basis of a CLIP model (ViT-14/L) pre-trained on LAION-2B. BioCLIP 2 yields state-of-the-art performance in recognizing various species. More importantly, it demonstrates emergent properties beyond species classification after extensive hierarchical contrastive training. Foundation models trained at scale exhibit emergent properties beyond their initial training objectives. BioCLIP 2 demonstrates such emergence beyond species classification by scaling up the hierarchical contrastive training proposed by BioCLIP. The model is trained on TreeOfLife-200M (the largest and most diverse available dataset of biology images). We evaluate BioCLIP 2 on a diverse set of biological tasks. Through training at scale, BioCLIP 2 improves species classification by 18.1% over BioCLIP. More importantly, we demonstrate that BioCLIP 2 generalizes to diverse biological questions beyond species classification solely through species-level supersvision. Further analysis reveals that BioCLIP 2 acquires two emergent properties through scaling up hierarchical contrastive learning: inter-species ecological alignment and intra-species variation separation.

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multimodallicense:mit
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档案更新时间2026/10/29 15:23:20
OVERVIEW

模型介绍

/resolve/main/ )| |:--| |**Figure #.** Image of .| --> BioCLIP 2 is a foundation model for biology organismal images. It is trained on TreeOfLife-200M on the basis of a CLIP model (ViT-14/L) pre-trained on LAION-2B. BioCLIP 2 yields state-of-the-art performance in recognizing various species. More importantly, it demonstrates emergent properties beyond species classification after extensive hierarchical contrastive training. Foundation models trained at scale exhibit emergent properties beyond their initial training objectives. BioCLIP 2 demonstrates such emergence beyond species classification by scaling up the hierarchical contrastive training proposed by BioCLIP. The model is trained on TreeOfLife-200M (the largest and most diverse available dataset of biology images). We evaluate BioCLIP 2 on a diverse set of biological tasks. Through training at scale, BioCLIP 2 improves species classification by 18.1% over BioCLIP. More importantly, we demonstrate that BioCLIP 2 generalizes to diverse biological questions beyond species classification solely through species-level supersvision. Further analysis reveals that BioCLIP 2 acquires two emergent properties through scaling up hierarchical contrastive learning: inter-species ecological alignment and intra-species variation separation.

主要特点

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局限与注意事项

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PRICING & COST

来源报价与费用分析

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INDEPENDENT BENCHMARKS

公开评测与运行配置

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常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

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资料来源与可追溯记录

许可证:mit。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。