timm documentation
EVA
EVA
类别: ViT · 代码: timm/models/eva.py · 架构: 65 · 预训练权重: 83(其中 28 个包含 ImageNet-1k 结果)
EVA 和 EVA-02 视觉 Transformer 以及其他共享 Eva 类的 ViT 变体,支持旋转位置编码 (RoPE)、SwiGLU MLP 及相关扩展。该模块还包含 Perception Encoder (PE)、ROPE-ViT、DINOv3、LingBot-Vision 和 Sapiens2 模型,以及带有 RoPE 的 timm 原始 ViT 配置。
论文
- EVA:探索大规模掩码视觉表示学习的极限
- EVA-02:《新世纪福音战士》的视觉呈现
- EVA-CLIP:大规模 CLIP 的改进训练技术
- Perception Encoder:最佳的视觉嵌入并不在网络的输出端
- 用于视觉 Transformer 的旋转位置嵌入
- DINOv3
- 用于稠密空间感知的视觉预训练
- Sapiens2
用法
请参阅快速入门了解如何加载预训练权重、进行图像预处理和微调,以及查阅特征提取了解如何将模型用作骨干网络。
>>> import timm
>>> model = timm.create_model('eva02_small_patch14_336.mim_in22k_ft_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)模型 API 支持
✓ 支持,✓ (square) 仅支持方形尺寸,✗ 不支持,– 无需支持(模型本身已接受任意输入尺寸)。通过在每个功能上运行随机初始化的模型(Eva 使用 vit_tiny_patch16_dinov3_qkvb,尺寸为 256)进行验证,预期同一类的其他架构表现相同。详情请参阅 Model API 页面。
架构
| 架构 | 参数(M) | GMAC | 输入大小 | 预训练权重 |
|---|---|---|---|---|
eva02_base_patch14_224 | 85.76 | 23.22 | 224 | 1 |
eva02_base_patch14_448 | 87.12 | 107.11 | 448 | 3 |
eva02_base_patch16_clip_224 | 86.26 | 17.62 | 224 | 1 |
eva02_enormous_patch14_clip_224 | 4350.56 | 1132.46 | 224 | 2 |
eva02_large_patch14_224 | 303.27 | 81.15 | 224 | 2 |
eva02_large_patch14_448 | 305.08 | 362.33 | 448 | 6 |
eva02_large_patch14_clip_224 | 304.11 | 81.18 | 224 | 1 |
eva02_large_patch14_clip_336 | 304.43 | 191.34 | 336 | 1 |
eva02_small_patch14_224 | 21.62 | 6.14 | 224 | 1 |
eva02_small_patch14_336 | 22.13 | 15.48 | 336 | 1 |
eva02_tiny_patch14_224 | 5.50 | 1.70 | 224 | 1 |
eva02_tiny_patch14_336 | 5.76 | 4.68 | 336 | 1 |
eva_giant_patch14_224 | 1012.56 | 267.18 | 224 | 1 |
eva_giant_patch14_336 | 1013.01 | 620.64 | 336 | 2 |
eva_giant_patch14_560 | 1014.45 | 1906.76 | 560 | 1 |
eva_giant_patch14_clip_224 | 1012.59 | 267.18 | 224 | 2 |
vit_5b_patch16_sapiens2 | 5066.90 | - | 1024x768 | 1 |
vit_7b_patch16_dinov3 | 6716.03 | 1775.10 | 256 | 2 |
vit_base_patch16_dinov3 | 85.64 | 23.60 | 256 | 1 |
vit_base_patch16_dinov3_qkvb | 85.66 | 23.60 | 256 | 2 |
vit_base_patch16_lingbot | 85.66 | - | 512 | 1 |
vit_base_patch16_rope_224 | 86.43 | 17.58 | 224 | 1 |
vit_base_patch16_rope_ape_224 | 86.59 | 17.58 | 224 | 1 |
vit_base_patch16_rope_mixed_224 | 86.44 | 17.58 | 224 | 1 |
vit_base_patch16_rope_mixed_ape_224 | 86.59 | 17.58 | 224 | 1 |
vit_base_patch16_rope_reg1_gap_256 | 86.43 | 23.22 | 256 | 1 |
vit_base_patch16_sapiens2 | 113.99 | - | 1024x768 | 1 |
vit_betwixt_patch16_rope_reg4_gap_256 | 60.23 | 16.52 | 256 | 1 |
vit_giant_patch16_lingbot | 1134.47 | - | 512 | 1 |
vit_giant_patch16_sapiens2 | 1455.47 | - | 1024x768 | 1 |
vit_huge_patch16_sapiens2 | 814.28 | - | 1024x768 | 1 |
vit_huge_plus_patch16_dinov3 | 840.51 | 224.88 | 256 | 1 |
vit_huge_plus_patch16_dinov3_qkvb | 840.59 | 224.88 | 256 | 1 |
vit_large_patch16_dinov3 | 303.08 | 82.43 | 256 | 2 |
vit_large_patch16_dinov3_qkvb | 303.13 | 82.43 | 256 | 2 |
vit_large_patch16_lingbot | 303.13 | - | 512 | 1 |
vit_large_patch16_rope_224 | 304.17 | 61.60 | 224 | 1 |
vit_large_patch16_rope_ape_224 | 304.37 | 61.60 | 224 | 1 |
vit_large_patch16_rope_mixed_224 | 304.20 | 61.60 | 224 | 1 |
vit_large_patch16_rope_mixed_ape_224 | 304.40 | 61.60 | 224 | 1 |
vit_large_patch16_sapiens2 | 395.45 | - | 1024x768 | 1 |
vit_medium_patch16_rope_reg1_gap_256 | 38.74 | 10.63 | 256 | 1 |
vit_mediumd_patch16_rope_reg1_gap_256 | 63.95 | 17.65 | 256 | 1 |
vit_pe_core_base_patch16_224 | 93.67 | 17.82 | 224 | 1 |
vit_pe_core_gigantic_patch14_448 | 1882.03 | 2060.12 | 448 | 1 |
vit_pe_core_large_patch14_336 | 317.15 | 192.33 | 336 | 1 |
vit_pe_core_small_patch16_384 | 23.78 | 15.69 | 384 | 1 |
vit_pe_core_tiny_patch16_384 | 6.14 | 4.74 | 384 | 1 |
vit_pe_lang_gigantic_patch14_448 | 1740.92 | 1931.99 | 448 | 2 |
vit_pe_lang_large_patch14_448 | 291.42 | 346.99 | 448 | 2 |
vit_pe_spatial_base_patch16_512 | 86.43 | 107.13 | 512 | 1 |
vit_pe_spatial_gigantic_patch14_448 | 1851.89 | 2055.25 | 448 | 1 |
vit_pe_spatial_large_patch14_448 | 303.96 | 362.05 | 448 | 1 |
vit_pe_spatial_small_patch16_512 | 21.98 | 31.80 | 512 | 1 |
vit_pe_spatial_tiny_patch16_512 | 5.68 | 10.46 | 512 | 1 |
vit_small_patch16_dinov3 | 21.59 | 6.26 | 256 | 1 |
vit_small_patch16_dinov3_qkvb | 21.60 | 6.26 | 256 | 2 |
vit_small_patch16_lingbot | 21.60 | - | 512 | 1 |
vit_small_patch16_rope_224 | 21.98 | 4.61 | 224 | 1 |
vit_small_patch16_rope_ape_224 | 22.06 | 4.61 | 224 | 1 |
vit_small_patch16_rope_mixed_224 | 21.99 | 4.61 | 224 | 1 |
vit_small_patch16_rope_mixed_ape_224 | 22.06 | 4.61 | 224 | 1 |
vit_small_plus_patch16_dinov3 | 28.68 | 8.11 | 256 | 1 |
vit_small_plus_patch16_dinov3_qkvb | 28.69 | 8.11 | 256 | 1 |
vit_tiny_patch16_dinov3_qkvb | 5.49 | - | 256 | 1 |
GMACs 和参数量对应于每种架构的默认输入大小和分类器,数据来自基准测试结果。
预训练权重
Top-1 是 results-imagenet.csv 中在所列评估规模下的 ImageNet-1k 验证准确率,- 表示该权重没有 ImageNet-1k 结果(例如预训练权重或图文权重)。Classes 是预训练分类器的大小,对于没有分类器的权重则为 none。
权重按来源分组:预训练配置中给出的论文或来源,否则为 timm 以外的 Hub 组织,否则为预训练标签的第一部分(architecture.tag)。仅包含单个权重的来源列在
other 下。
mim 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
eva02_base_patch14_224.mim_in22k | 224 | - | - | none | mit | timm |
eva02_base_patch14_448.mim_in22k_ft_in1k | 448 | 88.262 | 448 | 1000 | mit | timm |
eva02_base_patch14_448.mim_in22k_ft_in22k | 448 | - | - | 21841 | mit | timm |
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k | 448 | 88.678 | 448 | 1000 | mit | timm |
eva02_large_patch14_224.mim_in22k | 224 | - | - | none | mit | timm |
eva02_large_patch14_224.mim_m38m | 224 | - | - | none | mit | timm |
eva02_large_patch14_448.mim_in22k_ft_in1k | 448 | 89.634 | 448 | 1000 | mit | timm |
eva02_large_patch14_448.mim_in22k_ft_in22k | 448 | - | - | 21841 | mit | timm |
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k | 448 | 89.956 | 448 | 1000 | mit | timm |
eva02_large_patch14_448.mim_m38m_ft_in1k | 448 | 89.550 | 448 | 1000 | mit | timm |
eva02_large_patch14_448.mim_m38m_ft_in22k | 448 | - | - | 21841 | mit | timm |
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k | 448 | 90.056 | 448 | 1000 | mit | timm |
eva02_small_patch14_224.mim_in22k | 224 | - | - | none | mit | timm |
eva02_small_patch14_336.mim_in22k_ft_in1k | 336 | 85.770 | 336 | 1000 | mit | timm |
eva02_tiny_patch14_224.mim_in22k | 224 | - | - | none | mit | timm |
eva02_tiny_patch14_336.mim_in22k_ft_in1k | 336 | 80.648 | 336 | 1000 | mit | timm |
fb 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_pe_core_base_patch16_224.fb | 224 | - | - | 1024 | apache-2.0 | timm |
vit_pe_core_gigantic_patch14_448.fb | 448 | - | - | 1280 | apache-2.0 | timm |
vit_pe_core_large_patch14_336.fb | 336 | - | - | 1024 | apache-2.0 | timm |
vit_pe_core_small_patch16_384.fb | 384 | - | - | 512 | apache-2.0 | timm |
vit_pe_core_tiny_patch16_384.fb | 384 | - | - | 512 | apache-2.0 | timm |
vit_pe_lang_gigantic_patch14_448.fb | 448 | - | - | none | apache-2.0 | timm |
vit_pe_lang_gigantic_patch14_448.fb_tiling | 448 | - | - | none | apache-2.0 | timm |
vit_pe_lang_large_patch14_448.fb | 448 | - | - | none | apache-2.0 | timm |
vit_pe_lang_large_patch14_448.fb_tiling | 448 | - | - | none | apache-2.0 | timm |
vit_pe_spatial_base_patch16_512.fb | 512 | - | - | none | apache-2.0 | timm |
vit_pe_spatial_gigantic_patch14_448.fb | 448 | - | - | none | apache-2.0 | timm |
vit_pe_spatial_large_patch14_448.fb | 448 | - | - | none | apache-2.0 | timm |
vit_pe_spatial_small_patch16_512.fb | 512 | - | - | none | apache-2.0 | timm |
vit_pe_spatial_tiny_patch16_512.fb | 512 | - | - | none | apache-2.0 | timm |
naver 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_base_patch16_rope_224.naver_in1k | 224 | 83.714 | 224 | 1000 | apache-2.0 | timm |
vit_base_patch16_rope_ape_224.naver_in1k | 224 | 83.780 | 224 | 1000 | apache-2.0 | timm |
vit_base_patch16_rope_mixed_224.naver_in1k | 224 | 83.806 | 224 | 1000 | apache-2.0 | timm |
vit_base_patch16_rope_mixed_ape_224.naver_in1k | 224 | 83.898 | 224 | 1000 | apache-2.0 | timm |
vit_large_patch16_rope_224.naver_in1k | 224 | 84.646 | 224 | 1000 | apache-2.0 | timm |
vit_large_patch16_rope_ape_224.naver_in1k | 224 | 84.660 | 224 | 1000 | apache-2.0 | timm |
vit_large_patch16_rope_mixed_224.naver_in1k | 224 | 84.840 | 224 | 1000 | apache-2.0 | timm |
vit_large_patch16_rope_mixed_ape_224.naver_in1k | 224 | 84.846 | 224 | 1000 | apache-2.0 | timm |
vit_small_patch16_rope_224.naver_in1k | 224 | 81.222 | 224 | 1000 | apache-2.0 | timm |
vit_small_patch16_rope_ape_224.naver_in1k | 224 | 81.016 | 224 | 1000 | apache-2.0 | timm |
vit_small_patch16_rope_mixed_224.naver_in1k | 224 | 81.252 | 224 | 1000 | apache-2.0 | timm |
vit_small_patch16_rope_mixed_ape_224.naver_in1k | 224 | 80.974 | 224 | 1000 | apache-2.0 | timm |
lvd1689m 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_7b_patch16_dinov3.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_base_patch16_dinov3.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_base_patch16_dinov3_qkvb.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_huge_plus_patch16_dinov3.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_huge_plus_patch16_dinov3_qkvb.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_large_patch16_dinov3.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_large_patch16_dinov3_qkvb.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_small_patch16_dinov3.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_small_patch16_dinov3_qkvb.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_small_plus_patch16_dinov3.lvd1689m | 256 | - | - | none | dinov3-license | timm |
vit_small_plus_patch16_dinov3_qkvb.lvd1689m | 256 | - | - | none | dinov3-license | timm |
Sapiens2
预训练配置引用了 Sapiens2。
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_5b_patch16_sapiens2.fb | 1024x768 | - | - | none | sapiens2-license | timm |
vit_base_patch16_sapiens2.fb | 1024x768 | - | - | none | sapiens2-license | timm |
vit_giant_patch16_sapiens2.fb | 1024x768 | - | - | none | sapiens2-license | timm |
vit_huge_patch16_sapiens2.fb | 1024x768 | - | - | none | sapiens2-license | timm |
vit_large_patch16_sapiens2.fb | 1024x768 | - | - | none | sapiens2-license | timm |
merged2b 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
eva02_base_patch16_clip_224.merged2b | 224 | - | - | 512 | mit | timm |
eva02_large_patch14_clip_224.merged2b | 224 | - | - | 768 | mit | timm |
eva02_large_patch14_clip_336.merged2b | 336 | - | - | 768 | mit | timm |
eva_giant_patch14_clip_224.merged2b | 224 | - | - | 1024 | mit | timm |
sbb 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_base_patch16_rope_reg1_gap_256.sbb_in1k | 256 | 83.872 | 256 | 1000 | mit | timm |
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k | 256 | 83.906 | 256 | 1000 | mit | timm |
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k | 256 | 83.810 | 256 | 1000 | mit | timm |
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k | 256 | 84.316 | 256 | 1000 | mit | timm |
面向稠密空间感知的视觉预训练
预训练配置引用了Vision Pretraining for Dense Spatial Perception。
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_base_patch16_lingbot.robbyant | 512 | - | - | none | apache-2.0 | belfner/vit_base_patch16_lingbot.robbyant |
vit_giant_patch16_lingbot.robbyant | 512 | - | - | none | apache-2.0 | belfner/vit_giant_patch16_lingbot.robbyant |
vit_large_patch16_lingbot.robbyant | 512 | - | - | none | apache-2.0 | belfner/vit_large_patch16_lingbot.robbyant |
vit_small_patch16_lingbot.robbyant | 512 | - | - | none | apache-2.0 | belfner/vit_small_patch16_lingbot.robbyant |
eupe 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_base_patch16_dinov3_qkvb.eupe_lvd1689m | 256 | - | - | none | fair-noncommercial-research-license | timm |
vit_small_patch16_dinov3_qkvb.eupe_lvd1689m | 256 | - | - | none | fair-noncommercial-research-license | timm |
vit_tiny_patch16_dinov3_qkvb.eupe_lvd1689m | 256 | - | - | none | fair-noncommercial-research-license | timm |
sat493m 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
vit_7b_patch16_dinov3.sat493m | 256 | - | - | none | dinov3-license | timm |
vit_large_patch16_dinov3.sat493m | 256 | - | - | none | dinov3-license | timm |
vit_large_patch16_dinov3_qkvb.sat493m | 256 | - | - | none | dinov3-license | timm |
剪辑标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
eva_giant_patch14_224.clip_ft_in1k | 224 | 88.896 | 224 | 1000 | mit | timm |
eva_giant_patch14_336.clip_ft_in1k | 336 | 89.462 | 336 | 1000 | mit | timm |
laion2b 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
eva02_enormous_patch14_clip_224.laion2b | 224 | - | - | 1024 | mit | timm |
eva02_enormous_patch14_clip_224.laion2b_plus | 224 | - | - | 1024 | mit | timm |
m30m 标签
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
eva_giant_patch14_336.m30m_ft_in22k_in1k | 336 | 89.566 | 336 | 1000 | mit | timm |
eva_giant_patch14_560.m30m_ft_in22k_in1k | 560 | 89.790 | 560 | 1000 | mit | timm |
其他
| 权重 | 输入大小 | Top-1(%) | 评估大小 | 类 | 许可证 | Hub |
|---|---|---|---|---|---|---|
eva_giant_patch14_clip_224.laion400m | 224 | - | - | 1024 | mit | timm |
引用
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2211.07636},
year={2022}
}
@article{EVA02,
title={EVA-02: A Visual Representation for Neon Genesis},
author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
journal={arXiv preprint arXiv:2303.11331},
year={2023}
}
@article{bolya2025perception,
title={Perception encoder: The best visual embeddings are not at the output of the network},
author={Bolya, Daniel and Huang, Po-Yao and Sun, Peize and Cho, Jang Hyun and Madotto, Andrea and Wei, Chen and Ma,
Tengyu and Zhi, Jiale and Rajasegaran, Jathushan and Rasheed, Hanoona and others},
journal={arXiv preprint arXiv:2504.13181},
year={2025}
}
@inproceedings{heo2024rotary,
title={Rotary position embedding for vision transformer},
author={Heo, Byeongho and Park, Song and Han, Dongyoon and Yun, Sangdoo},
booktitle={European Conference on Computer Vision},
pages={289--305},
year={2024},
organization={Springer}
}
@article{simeoni2025dinov3,
title={{DINOv3}},
author={Sim{'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime
and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l
and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{'e}e
and Moutakanni, Th{'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie
and Brandt, John and Couprie, Camille and Mairal, Julien and J{'e}gou, Herv{'e} and Labatut, Patrick
and Bojanowski, Piotr},
year={2025},
eprint={2508.10104},
url={https://arxiv.org/abs/2508.10104},
}
@article{lingbot-vision2026,
title={Vision Pretraining for Dense Spatial Perception},
author={Fu, Zelin and Tan, Bin and Sun, Changjiang and Liu, Shaohui and Zheng, Kecheng and Xu, Yinghao
and Zhu, Xing and Shen, Yujun and Xue, Nan},
journal={arXiv preprint arXiv:2607.05247},
year={2026}
}
@article{khirodkarsapiens2,
title={Sapiens2},
author={Khirodkar, Rawal and Wen, He and Martinez, Julieta and Dong, Yuan and Su, Zhaoen and Saito, Shunsuke},
journal={arXiv preprint arXiv:2604.21681},
year={2026}
}