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2.24.0

  • Fix missing space in error message
  • use model flag for normalizing embeddings
  • init logit_bias for non siglip pretrained models
  • Fix logit_bias load_checkpoint addition
  • Make CoCa model match CLIP models for logit scale/bias init
  • Fix missing return of "logit_bias" in CoCa.forward
  • Add NLLB-CLIP with SigLIP models
  • Add get_logits method and NLLB tokenizer
  • Remove the empty file src/open_clip/generation_utils.py
  • Update params.py: "BatchNorm" -> "LayerNorm" in the description string for "--lock-text-freeze-layer-norm"

2.23.0

  • Add CLIPA-v2 models
  • Add SigLIP models
  • Add MetaCLIP models
  • Add NLLB-CLIP models
  • CLIPA train code
  • Minor changes/fixes
    • Remove protobuf version limit
    • Stop checking model name when loading CoCa models
    • Log native wandb step
    • Use bool instead of long masks

2.21.0

  • Add SigLIP loss + training support
  • Add more DataComp models (B/16, B/32 and B/32@256)
  • Update default num workers
  • Update CoCa generation for transformers>=4.31
  • PyTorch 2.0 state_dict() compatibility fix for compiled models
  • Fix padding in ResizeMaxSize
  • Convert JIT model on state dict load for pretrained='filename…'
  • Other minor changes and fixes (typos, README, dependencies, CI)

2.20.0

  • Add EVA models
  • Support serial worker training
  • Fix Python 3.7 compatibility

2.19.0

  • Add DataComp models

2.18.0

  • Enable int8 inference without .weight attribute

2.17.2

  • Update push_to_hf_hub

2.17.0

  • Add int8 support
  • Update notebook demo
  • Refactor zero-shot classification code

2.16.2

  • Fixes for context_length and vocab_size attributes

2.16.1

  • Fixes for context_length and vocab_size attributes
  • Fix --train-num-samples logic
  • Add HF BERT configs for PubMed CLIP model

2.16.0

  • Add improved g-14 weights
  • Update protobuf version

2.15.0

  • Add convnext_xxlarge weights
  • Fixed import in readme
  • Add samples per second per gpu logging
  • Fix slurm example

2.14.0

  • Move dataset mixtures logic to shard level
  • Fix CoCa accum-grad training
  • Safer transformers import guard
  • get_labels refactoring

2.13.0

  • Add support for dataset mixtures with different sampling weights
  • Make transformers optional again

2.12.0

  • Updated convnext configs for consistency
  • Added input_patchnorm option
  • Clean and improve CoCa generation
  • Support model distillation
  • Add ConvNeXt-Large 320x320 fine-tune weights

2.11.1

  • Make transformers optional
  • Add MSCOCO CoCa finetunes to pretrained models

2.11.0

  • coca support and weights
  • ConvNeXt-Large weights

2.10.1

  • hf-hub:org/model_id support for loading models w/ config and weights in Hugging Face Hub

2.10.0

  • Added a ViT-bigG-14 model.
  • Added an up-to-date example slurm script for large training jobs.
  • Added a option to sync logs and checkpoints to S3 during training.
  • New options for LR schedulers, constant and constant with cooldown
  • Fix wandb autoresuming when resume is not set
  • ConvNeXt base & base_w pretrained models added
  • timm- model prefix removed from configs
  • timm augmentation + regularization (dropout / drop-path) supported

2.9.3

  • Fix wandb collapsing multiple parallel runs into a single one

2.9.2

  • Fix braceexpand memory explosion for complex webdataset urls

2.9.1

  • Fix release

2.9.0

  • Add training feature to auto-resume from the latest checkpoint on restart via --resume latest
  • Allow webp in webdataset
  • Fix logging for number of samples when using gradient accumulation
  • Add model configs for convnext xxlarge

2.8.2

  • wrapped patchdropout in a torch.nn.Module

2.8.1

  • relax protobuf dependency
  • override the default patch dropout value in 'vision_cfg'

2.8.0

  • better support for HF models
  • add support for gradient accumulation
  • CI fixes
  • add support for patch dropout
  • add convnext configs

2.7.0

  • add multilingual H/14 xlm roberta large

2.6.1

  • fix setup.py _read_reqs

2.6.0

  • Make openclip training usable from pypi.
  • Add xlm roberta large vit h 14 config.

2.5.0

  • pretrained B/32 xlm roberta base: first multilingual clip trained on laion5B
  • pretrained B/32 roberta base: first clip trained using an HF text encoder

2.4.1

  • Add missing hf_tokenizer_name in CLIPTextCfg.

2.4.0

  • Fix #211, missing RN50x64 config. Fix type of dropout param for ResNet models
  • Bring back LayerNorm impl that casts to input for non bf16/fp16
  • zero_shot.py: set correct tokenizer based on args
  • training/params.py: remove hf params and get them from model config

2.3.1

  • Implement grad checkpointing for hf model.
  • custom_text: True if hf_model_name is set
  • Disable hf tokenizer parallelism

2.3.0

  • Generalizable Text Transformer with HuggingFace Models (@iejMac)

2.2.0

  • Support for custom text tower
  • Add checksum verification for pretrained model weights

2.1.0

  • lot including sota models, bfloat16 option, better loading, better metrics

1.2.0

  • ViT-B/32 trained on Laion2B-en
  • add missing openai RN50x64 model

1.1.1

  • ViT-B/16+
  • Add grad checkpointing support
  • more robust data loader