Bump lightly from 1.5.7 to 1.5.8 in /requirements

[dependabot[bot]]

Bumps lightly from 1.5.7 to 1.5.8.

Release notes

Sourced from lightly's releases.

Numpy version compatibility

Two changes w.r.t numpy version 2:

• Make lightly itself support numpy version 2: lightly-ai/lightly#1561
• Disallow numpy 2.0 in the requirements, as torchvision is not yet compatible with numpy 2: lightly-ai/lightly#1562

For more context, see lightly-ai/lightly#1558

Models

• AIM: Scalable Pre-training of Large Autoregressive Image Models
• Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021
• Bootstrap your own latent: A new approach to self-supervised Learning, 2020
• DCL: Decoupled Contrastive Learning, 2021
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022
• I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture, 2023
• MAE: Masked Autoencoders Are Scalable Vision Learners, 2021
• MSN: Masked Siamese Networks for Label-Efficient Learning, 2022
• MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019
• NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021
• PMSN: Prior Matching for Siamese Networks, 2022
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• SimMIM: A Simple Framework for Masked Image Modeling, 2021
• SimSiam: Exploring Simple Siamese Representation Learning, 2020
• SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022
• SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020
• TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022
• VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022

Commits

• 8346959 Bump version to 1.5.8 (#1563)
• f9e353c Change np.float_ to np.float64 (#1561)
• ffe5b56 Require NumPy 1.x (#1562)
• See full diff in compare view

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