add changelog (#236)

README.md

@@ -60,13 +60,16 @@ The repo name detrex has several interpretations:
 - <font color=#008000> <b> de-t.rex </b> </font>: de means 'the' in Dutch. T.rex, also called Tyrannosaurus Rex, means 'king of the tyrant lizards' and connects to our research work 'DINO', which is short for Dinosaur.
 
 ## What's New
-v0.2.1 was released on 01/02/2023:
-- Support **MaskDINO** coco instance segmentation.
-- Support new **DINO** baselines: `ViTDet-DINO`, `Focal-DINO`.
-- Support `FocalNet` Backbone.
-- Add tutorial about `downloading pretrained backbones`, `verify installation`.
-- Modified learning rate scheduler usage and add tutorial on `customized scheduler`.
-- Add more readable logging information for criterion and matcher.
+v0.3.0 was released on 03/17/2023:
+- Support new algorithms including `Anchor-DETR` and `DETA`.
+- Release more than 10+ pretrained models (including the converted weights): `DETR-R50 & R101`, `DETR-R50 & R101-DC5`, `DAB-DETR-R50 & R101-DC5`, `DAB-DETR-R50-3patterns`, `Conditional-DETR-R50 & R101-DC5`, `DN-DETR-R50-DC5`, `Anchor-DETR` and the `DETA-Swin-o365-finetune` model which can achieve **`62.9AP`** on coco val.
+- Support **MaskDINO** on ADE20k semantic segmentation task.
+- Support `EMAHook` during training by setting `train.model_ema.enabled=True`, which can enhance the model performance. DINO with EMA can achieve **`49.4AP`** with only 12epoch training.
+- Support mixed precision training by setting `train.amp.enabled=True`, which will **reduce 20% to 30% GPU memory usage**.
+- Support `train.fast_dev_run=True` for **fast debugging**.
+- Support **encoder-decoder checkpoint** in DINO, which may reduce **30% GPU** memory usage.
+- Support a great `slurm training scripts` by @rayleizhu, please check this issue for more details [#213](https://github.com/IDEA-Research/detrex/issues/213)
+
 
 Please see [changelog.md](./changlog.md) for details and release history.
 

changlog.md

@@ -1,5 +1,16 @@
 ## Change Log
 
+### v0.3.0 (17/03/2023)
+- Support new algorithms including `Anchor-DETR` and `DETA`.
+- Release more than 10+ pretrained models (including the converted weights): `DETR-R50 & R101`, `DETR-R50 & R101-DC5`, `DAB-DETR-R50 & R101-DC5`, `DAB-DETR-R50-3patterns`, `Conditional-DETR-R50 & R101-DC5`, `DN-DETR-R50-DC5`, `Anchor-DETR` and the `DETA-Swin-o365-finetune` model which can achieve **`62.9AP`** on coco val.
+- Support **MaskDINO** on ADE20k semantic segmentation task.
+- Support `EMAHook` during training by setting `train.model_ema.enabled=True`, which can enhance the model performance. DINO with EMA can achieve **`49.4AP`** with only 12epoch training.
+- Support mixed precision training by setting `train.amp.enabled=True`, which will **reduce 20% to 30% GPU memory usage**.
+- Support `train.fast_dev_run=True` for **fast debugging**.
+- Support **encoder-decoder checkpoint** in DINO, which may reduce **30% GPU** memory usage.
+- Support a great slurm training scripts by @rayleizhu, please check this issue for more details [#213](https://github.com/IDEA-Research/detrex/issues/213)
+
+
 ### v0.2.1 (01/02/2023)
 #### New Algorithm
 - MaskDINO COCO instance-seg/panoptic-seg pre-release [#154](https://github.com/IDEA-Research/detrex/pull/154)

docs/source/changelog.md

@@ -1,5 +1,16 @@
 ## Change Log
 
+### v0.3.0 (17/03/2023)
+- Support new algorithms including `Anchor-DETR` and `DETA`.
+- Release more than 10+ pretrained models (including the converted weights): `DETR-R50 & R101`, `DETR-R50 & R101-DC5`, `DAB-DETR-R50 & R101-DC5`, `DAB-DETR-R50-3patterns`, `Conditional-DETR-R50 & R101-DC5`, `DN-DETR-R50-DC5`, `Anchor-DETR` and the `DETA-Swin-o365-finetune` model which can achieve **`62.9AP`** on coco val.
+- Support **MaskDINO** on ADE20k semantic segmentation task.
+- Support `EMAHook` during training by setting `train.model_ema.enabled=True`, which can enhance the model performance. DINO with EMA can achieve **`49.4AP`** with only 12epoch training.
+- Support mixed precision training by setting `train.amp.enabled=True`, which will **reduce 20% to 30% GPU memory usage**.
+- Support `train.fast_dev_run=True` for **fast debugging**.
+- Support **encoder-decoder checkpoint** in DINO, which may reduce **30% GPU** memory usage.
+- Support a great slurm training scripts by @rayleizhu, please check this issue for more details [#213](https://github.com/IDEA-Research/detrex/issues/213)
+
+
 ### v0.2.1 (01/02/2023)
 #### New Algorithm
 - MaskDINO COCO instance-seg/panoptic-seg pre-release [#154](https://github.com/IDEA-Research/detrex/pull/154)

setup.py

@@ -31,7 +31,7 @@
 from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
 
 # detrex version info
-version = "0.2.1"
+version = "0.3.0"
 package_name = "detrex"
 cwd = os.path.dirname(os.path.abspath(__file__))