timm documentation

EVA

Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

EVA

카테고리: ViT · 코드: timm/models/eva.py · 아키텍처: 65 · 프리트레인드 가중치: 83 (ImageNet-1k 결과가 포함된 28개)

Eva 클래스를 공유하는 EVA 및 EVA-02 Vision Transformer와 기타 ViT 변형으로, 회전 위치 임베딩(RoPE), SwiGLU MLP 및 관련 추가 기능이 있습니다. 이 모듈에는 Perception Encoder(PE), ROPE-ViT, DINOv3, LingBot-Vision 및 Sapiens2 모델과 RoPE가 적용된 timm의 원본 ViT 구성도 포함되어 있습니다.

논문

사용

사전 학습된 가중치 로드, 이미지 전처리 및 파인튜닝에 대해서는 Quickstart를, 모델을 백본으로 사용하는 방법에 대해서는 Feature Extraction을 참고하세요.

>>> 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)

모델 API 지원

기능Eva
features_only=True feature map✓ (strides 16, 16, 16)
forward_intermediates()✓
prune_intermediate_layers()✓
입력 크기 무관, 변경 불필요✓
set_input_size()✓
dynamic_img_size=True✓
set_grad_checkpointing()✓
레이어별 LR 감쇠(group_matcher())✓
reset_classifier()✓

✓ 지원됨, ✓ (정사각형) 정사각형 크기만 작동, ✗ 미지원, – 필요 없음(모델이 이미 모든 입력 크기를 허용함). 무작위로 초기화된 모델(Eva에 vit_tiny_patch16_dinov3_qkvb를 256으로)에서 각 기능을 실행하여 확인했습니다. 같은 클래스의 다른 아키텍처도 동일한 동작을 할 것으로 예상됩니다. 자세한 내용은 Model API 페이지를 참조하세요.

아키텍처

아키텍처매개변수 (M)GMAC입력 크기사전 학습된 가중치
eva02_base_patch14_22485.7623.222241
eva02_base_patch14_44887.12107.114483
eva02_base_patch16_clip_22486.2617.622241
eva02_enormous_patch14_clip_2244350.561132.462242
eva02_large_patch14_224303.2781.152242
eva02_large_patch14_448305.08362.334486
eva02_large_patch14_clip_224304.1181.182241
eva02_large_patch14_clip_336304.43191.343361
eva02_small_patch14_22421.626.142241
eva02_small_patch14_33622.1315.483361
eva02_tiny_patch14_2245.501.702241
eva02_tiny_patch14_3365.764.683361
eva_giant_patch14_2241012.56267.182241
eva_giant_patch14_3361013.01620.643362
eva_giant_patch14_5601014.451906.765601
eva_giant_patch14_clip_2241012.59267.182242
vit_5b_patch16_sapiens25066.90-1024x7681
vit_7b_patch16_dinov36716.031775.102562
vit_base_patch16_dinov385.6423.602561
vit_base_patch16_dinov3_qkvb85.6623.602562
vit_base_patch16_lingbot85.66-5121
vit_base_patch16_rope_22486.4317.582241
vit_base_patch16_rope_ape_22486.5917.582241
vit_base_patch16_rope_mixed_22486.4417.582241
vit_base_patch16_rope_mixed_ape_22486.5917.582241
vit_base_patch16_rope_reg1_gap_25686.4323.222561
vit_base_patch16_sapiens2113.99-1024x7681
vit_betwixt_patch16_rope_reg4_gap_25660.2316.522561
vit_giant_patch16_lingbot1134.47-5121
vit_giant_patch16_sapiens21455.47-1024x7681
vit_huge_patch16_sapiens2814.28-1024x7681
vit_huge_plus_patch16_dinov3840.51224.882561
vit_huge_plus_patch16_dinov3_qkvb840.59224.882561
vit_large_patch16_dinov3303.0882.432562
vit_large_patch16_dinov3_qkvb303.1382.432562
vit_large_patch16_lingbot303.13-5121
vit_large_patch16_rope_224304.1761.602241
vit_large_patch16_rope_ape_224304.3761.602241
vit_large_patch16_rope_mixed_224304.2061.602241
vit_large_patch16_rope_mixed_ape_224304.4061.602241
vit_large_patch16_sapiens2395.45-1024x7681
vit_medium_patch16_rope_reg1_gap_25638.7410.632561
vit_mediumd_patch16_rope_reg1_gap_25663.9517.652561
vit_pe_core_base_patch16_22493.6717.822241
vit_pe_core_gigantic_patch14_4481882.032060.124481
vit_pe_core_large_patch14_336317.15192.333361
vit_pe_core_small_patch16_38423.7815.693841
vit_pe_core_tiny_patch16_3846.144.743841
vit_pe_lang_gigantic_patch14_4481740.921931.994482
vit_pe_lang_large_patch14_448291.42346.994482
vit_pe_spatial_base_patch16_51286.43107.135121
vit_pe_spatial_gigantic_patch14_4481851.892055.254481
vit_pe_spatial_large_patch14_448303.96362.054481
vit_pe_spatial_small_patch16_51221.9831.805121
vit_pe_spatial_tiny_patch16_5125.6810.465121
vit_small_patch16_dinov321.596.262561
vit_small_patch16_dinov3_qkvb21.606.262562
vit_small_patch16_lingbot21.60-5121
vit_small_patch16_rope_22421.984.612241
vit_small_patch16_rope_ape_22422.064.612241
vit_small_patch16_rope_mixed_22421.994.612241
vit_small_patch16_rope_mixed_ape_22422.064.612241
vit_small_plus_patch16_dinov328.688.112561
vit_small_plus_patch16_dinov3_qkvb28.698.112561
vit_tiny_patch16_dinov3_qkvb5.49-2561

GMACs와 매개변수 수는 각 아키텍처의 기본 입력 크기 및 분류기에 대한 값이며, 벤치마크 결과에서 가져왔습니다.

사전 학습된 가중치

Top-1은 results-imagenet.csv에 나열된 평가 크기에서 측정된 ImageNet-1k 검증 정확도입니다. - 가중치에 ImageNet-1k 결과가 없는 경우(예: 사전 학습 또는 이미지-텍스트 가중치)에는 표시하지 않습니다. Classes는 사전 학습된 분류기의 크기를 나타내며, 분류기가 없는 가중치의 경우 none입니다.

Weights는 소스에 따라 그룹화됩니다. 사전 학습 설정에 명시된 논문 또는 출처, 그렇지 않으면 timm가 아닌 Hub 조직, 그렇지 않으면 사전 학습 태그의 첫 번째 부분(architecture.tag)입니다. 단일 weight만 있는 소스는 other에 나열됩니다.

mim 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
eva02_base_patch14_224.mim_in22k224--없음mittimm
eva02_base_patch14_448.mim_in22k_ft_in1k44888.2624481000mittimm
eva02_base_patch14_448.mim_in22k_ft_in22k448--21841mittimm
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k44888.6784481000mittimm
eva02_large_patch14_224.mim_in22k224--없음mittimm
eva02_large_patch14_224.mim_m38m224--없음mittimm
eva02_large_patch14_448.mim_in22k_ft_in1k44889.6344481000mittimm
eva02_large_patch14_448.mim_in22k_ft_in22k448--21841mittimm
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k44889.9564481000mittimm
eva02_large_patch14_448.mim_m38m_ft_in1k44889.5504481000mittimm
eva02_large_patch14_448.mim_m38m_ft_in22k448--21841mittimm
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k44890.0564481000mittimm
eva02_small_patch14_224.mim_in22k224--없음mittimm
eva02_small_patch14_336.mim_in22k_ft_in1k33685.7703361000mittimm
eva02_tiny_patch14_224.mim_in22k224--없음mittimm
eva02_tiny_patch14_336.mim_in22k_ft_in1k33680.6483361000mittimm

fb 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_pe_core_base_patch16_224.fb224--1024apache-2.0timm
vit_pe_core_gigantic_patch14_448.fb448--1280apache-2.0timm
vit_pe_core_large_patch14_336.fb336--1024apache-2.0timm
vit_pe_core_small_patch16_384.fb384--512apache-2.0timm
vit_pe_core_tiny_patch16_384.fb384--512apache-2.0timm
vit_pe_lang_gigantic_patch14_448.fb448--없음apache-2.0timm
vit_pe_lang_gigantic_patch14_448.fb_tiling448--없음apache-2.0timm
vit_pe_lang_large_patch14_448.fb448--없음apache-2.0timm
vit_pe_lang_large_patch14_448.fb_tiling448--없음apache-2.0timm
vit_pe_spatial_base_patch16_512.fb512--없음apache-2.0timm
vit_pe_spatial_gigantic_patch14_448.fb448--없음apache-2.0timm
vit_pe_spatial_large_patch14_448.fb448--없음apache-2.0timm
vit_pe_spatial_small_patch16_512.fb512--없음apache-2.0timm
vit_pe_spatial_tiny_patch16_512.fb512--없음apache-2.0timm

네이버 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_base_patch16_rope_224.naver_in1k22483.7142241000apache-2.0timm
vit_base_patch16_rope_ape_224.naver_in1k22483.7802241000apache-2.0timm
vit_base_patch16_rope_mixed_224.naver_in1k22483.8062241000apache-2.0timm
vit_base_patch16_rope_mixed_ape_224.naver_in1k22483.8982241000apache-2.0timm
vit_large_patch16_rope_224.naver_in1k22484.6462241000apache-2.0timm
vit_large_patch16_rope_ape_224.naver_in1k22484.6602241000apache-2.0timm
vit_large_patch16_rope_mixed_224.naver_in1k22484.8402241000apache-2.0timm
vit_large_patch16_rope_mixed_ape_224.naver_in1k22484.8462241000apache-2.0timm
vit_small_patch16_rope_224.naver_in1k22481.2222241000apache-2.0timm
vit_small_patch16_rope_ape_224.naver_in1k22481.0162241000apache-2.0timm
vit_small_patch16_rope_mixed_224.naver_in1k22481.2522241000apache-2.0timm
vit_small_patch16_rope_mixed_ape_224.naver_in1k22480.9742241000apache-2.0timm

lvd1689m 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_7b_patch16_dinov3.lvd1689m256--없음dinov3-licensetimm
vit_base_patch16_dinov3.lvd1689m256--없음dinov3-licensetimm
vit_base_patch16_dinov3_qkvb.lvd1689m256--없음dinov3-licensetimm
vit_huge_plus_patch16_dinov3.lvd1689m256--없음dinov3-licensetimm
vit_huge_plus_patch16_dinov3_qkvb.lvd1689m256--없음dinov3-licensetimm
vit_large_patch16_dinov3.lvd1689m256--없음dinov3-licensetimm
vit_large_patch16_dinov3_qkvb.lvd1689m256--없음dinov3-licensetimm
vit_small_patch16_dinov3.lvd1689m256--없음dinov3-licensetimm
vit_small_patch16_dinov3_qkvb.lvd1689m256--없음dinov3-licensetimm
vit_small_plus_patch16_dinov3.lvd1689m256--없음dinov3-licensetimm
vit_small_plus_patch16_dinov3_qkvb.lvd1689m256--없음dinov3-licensetimm

Sapiens2

사전 학습된 설정은 Sapiens2를 참조합니다.

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_5b_patch16_sapiens2.fb1024x768--없음sapiens2-licensetimm
vit_base_patch16_sapiens2.fb1024x768--없음sapiens2-licensetimm
vit_giant_patch16_sapiens2.fb1024x768--없음sapiens2-licensetimm
vit_huge_patch16_sapiens2.fb1024x768--없음sapiens2-licensetimm
vit_large_patch16_sapiens2.fb1024x768--없음sapiens2-licensetimm

merged2b 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
eva02_base_patch16_clip_224.merged2b224--512mittimm
eva02_large_patch14_clip_224.merged2b224--768mittimm
eva02_large_patch14_clip_336.merged2b336--768mittimm
eva_giant_patch14_clip_224.merged2b224--1024mittimm

sbb 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_base_patch16_rope_reg1_gap_256.sbb_in1k25683.8722561000mittimm
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k25683.9062561000mittimm
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k25683.8102561000mittimm
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k25684.3162561000mittimm

밀집 공간 지각을 위한 비전 사전 학습

Pretrained configs는 Vision Pretraining for Dense Spatial Perception를 참조합니다.

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_base_patch16_lingbot.robbyant512--없음apache-2.0belfner/vit_base_patch16_lingbot.robbyant
vit_giant_patch16_lingbot.robbyant512--없음apache-2.0belfner/vit_giant_patch16_lingbot.robbyant
vit_large_patch16_lingbot.robbyant512--없음apache-2.0belfner/vit_large_patch16_lingbot.robbyant
vit_small_patch16_lingbot.robbyant512--없음apache-2.0belfner/vit_small_patch16_lingbot.robbyant

eupe 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_base_patch16_dinov3_qkvb.eupe_lvd1689m256--없음fair-noncommercial-research-licensetimm
vit_small_patch16_dinov3_qkvb.eupe_lvd1689m256--없음fair-noncommercial-research-licensetimm
vit_tiny_patch16_dinov3_qkvb.eupe_lvd1689m256--없음fair-noncommercial-research-licensetimm

sat493m 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
vit_7b_patch16_dinov3.sat493m256--없음dinov3-licensetimm
vit_large_patch16_dinov3.sat493m256--없음dinov3-licensetimm
vit_large_patch16_dinov3_qkvb.sat493m256--없음dinov3-licensetimm

클립 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
eva_giant_patch14_224.clip_ft_in1k22488.8962241000mittimm
eva_giant_patch14_336.clip_ft_in1k33689.4623361000mittimm

laion2b 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
eva02_enormous_patch14_clip_224.laion2b224--1024mittimm
eva02_enormous_patch14_clip_224.laion2b_plus224--1024mittimm

m30m 태그

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
eva_giant_patch14_336.m30m_ft_in22k_in1k33689.5663361000mittimm
eva_giant_patch14_560.m30m_ft_in22k_in1k56089.7905601000mittimm

기타

가중치입력 크기Top-1(%)평가 크기클래스라이선스Hub
eva_giant_patch14_clip_224.laion400m224--1024mittimm

인용

@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}
}
Update on GitHub