title: 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
summary: 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.
paper_authors._id: 649b9db0c551eb1992d8a511
paper_authors.name: Xianhang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-28T07:45:02.825Z
paper_authors._id: 645e72d4b5c9a8666d0ddc1c
paper_authors.avatarUrl: /avatars/f0f1684731eb5ae148eadf841cceb18e.svg
paper_authors.fullname: xhl
paper_authors.type: user
paper_authors.user: Xianhang
paper_authors._id: 649b9db0c551eb1992d8a512
paper_authors.name: Zeyu Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-28T07:45:45.259Z
paper_authors._id: 6462879777f63463fc5f75f9
paper_authors.avatarUrl: /avatars/f19d14a009e940e3b565a6c62251599b.svg
paper_authors.fullname: Zeyu Wang
paper_authors.type: user
paper_authors.user: zw123
paper_authors._id: 649b9db0c551eb1992d8a513
paper_authors.name: Cihang Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-28T07:46:03.715Z
paper_authors._id: 645eb61da3c5cd8a16efffff
paper_authors.avatarUrl: /avatars/9112bfeed598dfabf9e077e69e09ecc9.svg
paper_authors.fullname: Cihang Xie
paper_authors.type: user
paper_authors.user: cihangxie
paper_title: 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
paper_summary: 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.
paper_ai_keywords: inverse scaling law CLIP training image/text encoders sequence length image/text tokens high-performance CLIP models computational needs finetuning stage CLIPA-v2 H/14 model image-text pairs zero-shot ImageNet accuracy OpenCLIP computational cost zero-shot ImageNet accuracy