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python313-timm-1.0.29-1.1 RPM for noarch

From OpenSuSE Tumbleweed for noarch

Name: python313-timm Distribution: openSUSE Tumbleweed
Version: 1.0.29 Vendor: openSUSE
Release: 1.1 Build date: Sat Aug 29 09:17:01 2026
Group: Unspecified Build host: reproducible
Size: 19739378 Source RPM: python-timm-1.0.29-1.1.src.rpm
Packager: https://bugs.opensuse.org
Url: https://github.com/huggingface/pytorch-image-models
Summary: PyTorch Image Models
 
PyTorch Image Models (timm) is a collection of image models, layers, utilities,
optimizers, schedulers, data-loaders / augmentations, and reference training /
validation scripts that aim to pull together a wide variety of SOTA models with
ability to reproduce ImageNet training results.

Provides

Requires

License

Apache-2.0

Changelog

* Sat Aug 29 2026 Martin Pluskal <mpluskal@suse.com>
  - Update to 1.0.29:
    * Add LowFormer, CPUBone, PP-LCNetV2 and LingBot-Vision model
      definitions and pretrained weights
    * Add per-batch image and batch size scheduling for non-NaFlex
      training (progressive small-to-large resolution schedules)
    * NaFlexViT: optional key-only attention masks, compile-friendly
      and memory-efficient position embedding sampling, cached
      variable-patch interpolation
    * Improve model factory path and checkpoint handling
    * Fix positional embedding resampling for equal-token grid
      changes, and classifier head device/dtype loss on reset
    * Fix CutMix minmax border sampling and other mixup/cutmix bbox
      range issues
    * Fix custom input channels for pruned EfficientNet models
    * Fix optimizer bugs: AdafactorBigVision factored row
      normalization, SGDW foreach weight decay, layer_decay without
      an explicit min scale
    * Fix auto-augment crashes on hparams=None and 'inc'/'b' config
      flags ignoring the documented 0 value
    * See upstream's release notes for the remaining minor fixes
* Mon Aug 24 2026 Martin Pluskal <mpluskal@suse.com>
  - Update to 1.0.28:
    * New models: DINOv3 ConvNeXt/ViT, Gemma4 ViT, TIPSv2, EUPE,
      CSATv2, MobileCLIP-2 and MetaCLIP-2 encoders, SigLIP-2
      NaFlex weights
    * Add the Muon optimizer plus AdaMuon/NAdaMuon variants, and
      a cautious mode for AdamP and SGDP
    * Add a lightweight task abstraction with logits, feature and
      token distillation support (new timm.task subpackage)
    * Add ROPE support to NaFlexViT and set_input_size() to EVA
      models; extend NaFlexViT patch layout and forward_intermediates
    * Add differential attention, coordinate attention and the
      LsePlus/SimPool/PRR pooling modules
    * Harden checkpoint loading: default weights_only=True and
      address several pickle security concerns
    * Support device/dtype factory kwargs and meta-device model
      initialisation
    * Fix LayerScale ignoring init_values, reg_token weight decay
      grouping, Hiera SDPA dispatch and a ZeroDivisionError in
      AverageMeter
    * Remove APEX AMP support and reduce torch.jit usage ahead of
      its deprecation
    * Compat break: correct QKV vs MLP bias in ParallelScalingBlock
      and DiffParallelScalingBlock; no shipped weights are affected
    * Compat break: MobileNet-V5 gains a stem bias and uses the tanh
      GELU approximation, breaking pre-1.0.17 weights
  - Extend the %check smoke test with a forward pass and a guard for
    the SigLIP ViT that python-sglang builds via timm.create_model()
* Fri Aug 21 2026 Martin Pluskal <mpluskal@suse.com>
  - Update to 1.0.16 (pinned by SGLang 0.5.17):
    * Add MobileNetV5 backbone for the Gemma 3n image encoder
    * Add NaFlexViT (variable aspect/resolution, FlexiViT and
      NaViT embeddings) plus a matching data pipeline
    * Add SigLIP 2, AIM-v2, SAM2 (HieraDet), BEiT-3, InternViT,
      MambaOut, PaliGemma2 and Meta Perception Encoder models
    * Add SwiftFormer, FasterNet, SHViT, StarNet, GhostNet-V3
      and RDNet architectures
    * New pretrained weights for MobileNet-V4, SBB/SO150M ViT,
      EfficientNet-B0/B1, ResNet50-D and others
    * Add local-dir: schema for loading Hub models from a
      local folder
    * Add set_input_size() for ViT and Swin v1/v2
    * Fix LeViT safetensors loading and the deprecated
      timm.models.registry import path
  - Switch Source to the PyPI sdist (timm-1.0.16.tar.gz), replacing
    python-timm-1.0.7.tar.gz
  - BuildRequires python-pdm-backend (pdm.backend) instead of
    python-pdm so the wheel actually builds
  - Add a %check import smoke test
  - Add runtime Requires for torch, torchvision, PyYAML,
    huggingface-hub and safetensors (generator does not pick
    them up from METADATA)
* Wed Jul 24 2024 Christian Goll <cgoll@suse.com>
  - inital commit of python-timm a helper fpr python-tranformers
    with images

Files

/usr/lib/python3.13/site-packages/timm
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/INSTALLER
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/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/RECORD
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/REQUESTED
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/WHEEL
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/entry_points.txt
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/licenses
/usr/lib/python3.13/site-packages/timm-1.0.29.dist-info/licenses/LICENSE
/usr/lib/python3.13/site-packages/timm/__init__.py
/usr/lib/python3.13/site-packages/timm/__pycache__
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Fabrice Bellet, Wed Sep 2 23:55:13 2026