SAM-CAT-Seg / open_clip /HISTORY.md
seokju cho
initial commit
f8f62f3
|
raw
history blame
2.54 kB
## 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