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热门探索

84,257 款模型档案12976 家厂商 / 发布者45 个能力分类464 款有可比报价 · 30 款有公开评测

T文本生成 ▧图像生成 ▷视频生成 ⌘代码生成 ◖语音合成 ♫音乐生成 ⬡3D 建模 ▤文档分析

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探索 AI 模型 4

厂商输入输出许可

为你发现值得关注的模型

biocap

imageomics

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

分类待补充

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BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology. It is trained on TreeOfLife-10M, our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date. Through rigorous benchmarking on a diverse set of fine-grained biological classification tasks, BioCLIP consistently outperformed existing baselines by 16% to 17% absolute. Through intrinsic evaluation, we found that BioCLIP learned a hierarchical representation aligned to the tree of life, which demonstrates its potential for robust generalizability. **See the `examples/` directory for examples of how to use BioCLIP in zero-shot and few-shot settings.** BioCLIP is based on OpenAI's CLIP. We trained the model on TreeOfLife-10M from OpenAI's ViT-B/16 checkpoint, using OpenCLIP's code. BioCLIP is trained with the standard CLIP objective to imbue the model with an understanding, not just of different species, but of the hierarchical structure that relates species across the tree of life. In this way, BioCLIP offers potential to aid biologists in discovery of new and related creatures, since it does not see the 454K different taxa as distinct classes, but as part of an interconnected hierarchy.

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bioclip-2

imageomics

/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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分类待补充

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bioclip-2.5-vith14

imageomics

BioCLIP 2.5 Huge is a foundation model for biology organismal images. It is trained on TreeOfLife-200M on the basis of a CLIP model (ViT-H/14) pre-trained on LAION-2B. BioCLIP 2.5 Huge yields state-of-the-art performance in recognizing various species and further improves the performance on broader biological visual tasks. BioCLIP 2.5 Huge is trained on an updated version of TreeOfLife-200M that adds 19M more images to the original version (dataset to be updated with new metadata and species embeddings). In addition to the slightly larger training dataset, BioCLIP 2.5 Huge was extended to a ViT-H/14 backbone, in comparison with the ViT-L/14 backbone adopted by BioCLIP 2. The training was based on an updated version of the BioCLIP 2 repository (v2.0.0), where `torchcompile` and `pure_bf16` were used for acceleration. The model achieves new state-of-the-art performance on both species classification and broader biological visual tasks, surpassing BioCLIP 2 by 5.7% and 3.5%, respectively. Especially in FishNet, which requires the model to distinguish different habitats, BioCLIP 2.5 Huge demonstrates a 8.7% performance improvement over BioCLIP 2.

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分类待补充

输入待核验→ 输出待核验

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

找到适合你的 AI 模型。

探索 AI 模型 4

biocap

bioclip

bioclip-2

bioclip-2.5-vith14

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。