CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy
  within a \10,000 Budget; An Extra 4,000 Unlocks 81.8% Accuracy
The recent work CLIPA presents an inverse scaling law for CLIP training --
whereby the larger the image/text encoders used, the shorter the sequence
length of image/text tokens that can be applied in training. This finding
enables us to train high-performance CLIP models with significantly reduced
computations. Building upon this work, we hereby present CLIPA-v2 with two key
contributions. Technically, we find this inverse scaling law is also applicable
in the finetuning stage, enabling further reduction in computational needs.
Empirically, we explore CLIPA at scale, extending the experiments up to the
H/14 model with ~13B image-text pairs seen during training.
  Our results are exciting -- by only allocating a budget of \10,000, our CLIP
model achieves an impressive zero-shot ImageNet accuracy of 81.1%, surpassing
the prior best CLIP model (from OpenCLIP, 80.1%) by 1.0% and meanwhile reducing
the computational cost by ~39X. Moreover, with an additional investment of
4,000, we can further elevate the zero-shot ImageNet accuracy to 81.8%. Our
code and models are available at https://github.com/UCSC-VLAA/CLIPA.
649b9db0c551eb1992d8a511
Xianhang Li
admin_assigned
2023-06-28T07:45:02.825Z
645e72d4b5c9a8666d0ddc1c
/avatars/f0f1684731eb5ae148eadf841cceb18e.svg
xhl
user
Xianhang
649b9db0c551eb1992d8a512
Zeyu Wang
admin_assigned
2023-06-28T07:45:45.259Z
6462879777f63463fc5f75f9
/avatars/f19d14a009e940e3b565a6c62251599b.svg
Zeyu Wang
user
zw123
649b9db0c551eb1992d8a513
Cihang Xie
admin_assigned
2023-06-28T07:46:03.715Z
645eb61da3c5cd8a16efffff
/avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
Cihang Xie
user
cihangxie
CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy
  within a \10,000 Budget; An Extra 4,000 Unlocks 81.8% Accuracy
The recent work CLIPA presents an inverse scaling law for CLIP training --
whereby the larger the image/text encoders used, the shorter the sequence
length of image/text tokens that can be applied in training. This finding
enables us to train high-performance CLIP models with significantly reduced
computations. Building upon this work, we hereby present CLIPA-v2 with two key
contributions. Technically, we find this inverse scaling law is also applicable
in the finetuning stage, enabling further reduction in computational needs.
Empirically, we explore CLIPA at scale, extending the experiments up to the
H/14 model with ~13B image-text pairs seen during training.
  Our results are exciting -- by only allocating a budget of \10,000, our CLIP
model achieves an impressive zero-shot ImageNet accuracy of 81.1%, surpassing
the prior best CLIP model (from OpenCLIP, 80.1%) by 1.0% and meanwhile reducing
the computational cost by ~39X. Moreover, with an additional investment of
4,000, we can further elevate the zero-shot ImageNet accuracy to 81.8%. Our
code and models are available at https://github.com/UCSC-VLAA/CLIPA.
CLIPA-v2 extends CLIPA by applying an inverse scaling law during fine-tuning, achieving high zero-shot ImageNet accuracy with reduced computational costs.
CLIPA inverse scaling law finetuning CLIP zero-shot ImageNet accuracy OpenCLIP computational cost