EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Scaling up contrastive language-image pretraining (CLIP) is critical for
empowering both vision and multimodal models. We present EVA-CLIP-18B, the
largest and most powerful open-source CLIP model to date, with 18-billion
parameters. With only 6-billion training samples seen, EVA-CLIP-18B achieves an
exceptional 80.7% zero-shot top-1 accuracy averaged across 27 widely recognized
image classification benchmarks, outperforming its forerunner EVA-CLIP
(5-billion parameters) and other open-source CLIP models by a large margin.
Remarkably, we observe a consistent performance improvement with the model size
scaling of EVA-CLIP, despite maintaining a constant training dataset of
2-billion image-text pairs from LAION-2B and COYO-700M. This dataset is openly
available and much smaller than the in-house datasets (e.g., DFN-5B, WebLI-10B)
employed in other state-of-the-art CLIP models. EVA-CLIP-18B demonstrates the
potential of EVA-style weak-to-strong visual model scaling. With our model
weights made publicly available, we hope to facilitate future research in
vision and multimodal foundation models.
65c2e6a7400d860a3ea74024
Quan Sun
admin_assigned
2024-02-12T09:11:53.801Z
630d7a8f81ef9b1772b67f4c
/avatars/00757abd6e548ccebb5bfb233be129a2.svg
Quan Sun
user
QuanSun
65c2e6a7400d860a3ea74025
Jinsheng Wang
admin_assigned
2024-02-12T09:12:00.529Z
6444a987af034cdfd69f6f8d
/avatars/36544a102771a80c4fc6cb39e0973b2f.svg
Jinsheng Wang
user
wolfwjs
65c2e6a7400d860a3ea74026
Qiying Yu
admin_assigned
2024-02-12T09:12:07.089Z
6341375be5071b0c5cc946a3
/avatars/c26a32b54a3f7731854059d247b5c523.svg
Qiying Yu
user
qiying
65c2e6a7400d860a3ea74027
Yufeng Cui
admin_assigned
2024-02-12T09:12:13.155Z
648683de623b5f050213f2be
/avatars/83ecbcf4a21f68d2893de79f0444d6e3.svg
Yufeng Cui
user
YufengCui
65c2e6a7400d860a3ea74028
Fan Zhang
claimed_verified
2024-10-08T07:22:53.816Z
640ed40dc025ddf618950af7
/avatars/a99abd4346fa649ad4144f284ebcc972.svg
Fan Zhang
user
ryanzhangfan
65c2e6a7400d860a3ea74029
Xiaosong Zhang
admin_assigned
2024-02-12T09:13:05.307Z
64e473f0b78bc92221ab1883
/avatars/85dd02c2b4879d7696b552f0f9dc2680.svg
Xiaosong Zhang
user
zhangxiaosong18
65c2e6a7400d860a3ea7402a
Xinlong Wang
admin_assigned
2024-02-12T09:13:12.906Z
63ca558304c979828311c5a5
/avatars/2a439d79fba2f987cabe780d10c94d25.svg
Xinlong Wang
user
xinlongwang
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Scaling up contrastive language-image pretraining (CLIP) is critical for
empowering both vision and multimodal models. We present EVA-CLIP-18B, the
largest and most powerful open-source CLIP model to date, with 18-billion
parameters. With only 6-billion training samples seen, EVA-CLIP-18B achieves an
exceptional 80.7% zero-shot top-1 accuracy averaged across 27 widely recognized
image classification benchmarks, outperforming its forerunner EVA-CLIP
(5-billion parameters) and other open-source CLIP models by a large margin.
Remarkably, we observe a consistent performance improvement with the model size
scaling of EVA-CLIP, despite maintaining a constant training dataset of
2-billion image-text pairs from LAION-2B and COYO-700M. This dataset is openly
available and much smaller than the in-house datasets (e.g., DFN-5B, WebLI-10B)
employed in other state-of-the-art CLIP models. EVA-CLIP-18B demonstrates the
potential of EVA-style weak-to-strong visual model scaling. With our model
weights made publicly available, we hope to facilitate future research in
vision and multimodal foundation models.
EVA-CLIP-18B, a 18-billion parameter open-source CLIP model, achieves exceptional zero-shot performance across multiple image classification benchmarks with a relatively small dataset.
contrastive language-image pretraining CLIP EVA-CLIP-18B zero-shot top-1 accuracy image classification LAION-2B COYO-700M visual model scaling multimodal foundation models