Awesome
Mask DINO <img src="figures/dinosaur.png" width="30">
Feng Li*, Hao Zhang*, Huaizhe Xu, Shilong Liu, Lei Zhang, Lionel M. Ni, and Heung-Yeung Shum.
This repository is the official implementation of the Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation (DINO pronounced `daɪnoʊ' as in dinosaur). Our code is based on detectron2. detrex version is opensource simultaneously.
:fire: We release a strong open-set object detection and segmentation model OpenSeeD based on MaskDINO that achieves the best results on open-set object segmentation tasks. Code and checkpoints are available here.
<details open> <summary> <font size=8><strong>News</strong></font> </summary>[2023/7] We release Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Code and checkpoint are available!
[2023/2] Mask DINO has been accepted to CVPR 2023!
[2022/9] We release a toolbox detrex that provides state-of-the-art Transformer-based detection algorithms. It includes DINO with better performance and Mask DINO will also be released with detrex implementation. Welcome to use it! </br>
- Supports Now: DETR, Deformble DETR, Conditional DETR, Group-DETR, DAB-DETR, DN-DETR, DINO.
[2022/7] Code for DINO is available here!
[2022/3]We build a repo Awesome Detection Transformer to present papers about transformer for detection and segmentation. Welcome to your attention!
</details>Features
- A unified architecture for object detection, panoptic, instance and semantic segmentation.
- Achieve task and data cooperation between detection and segmentation.
- State-of-the-art performance under the same setting.
- Support major detection and segmentation datasets: COCO, ADE20K, Cityscapes.
Code Updates
-
[2022/12/02] Our code and checkpoints are available! Mask DINO further Achieves <strong>51.7</strong> and <strong>59.0</strong> box AP on COCO with a ResNet-50 and SwinL without extra detection data, outperforming DINO under the same setting!
-
[2022/6] We propose a unified detection and segmentation model Mask DINO that achieves the best results on all the three segmentation tasks (54.7 AP on COCO instance leaderboard, 59.5 PQ on COCO panoptic leaderboard, and 60.8 mIoU on ADE20K semantic leaderboard)!
-
Release code and checkpoints
-
Release model conversion checkpointer from DINO to MaskDINO
-
Release GPU cluster submit scripts based on submitit for multi-node training
-
Release EMA training for large models
-
Release more large models
Installation
See installation instructions.
Getting Started
See Inference Demo with Pre-trained Model
See Results.
See Preparing Datasets for MaskDINO.
See Getting Started.
See More Usage.
Results
In this part, we present the clean models that do not use extra detection data or tricks.
COCO Instance Segmentation and Object Detection
we follow DINO to use hidden dimension 2048
in the encoder of feedforward by default. We also use the mask-enhanced
box initialization proposed in our paper in instance segmentation and detection. To better present our model, we also list the models trained with
hidden dimension 1024
(hid 1024
) and not using mask-enhance initialization (no mask enhance
) in this table.
COCO Panoptic Segmentation
<table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Name</th> <th valign="bottom">Backbone</th> <th valign="bottom">epochs</th> <th valign="bottom">PQ</th> <th valign="bottom">Mask AP</th> <th valign="bottom">Box AP</th> <th valign="bottom">mIoU</th> <th valign="bottom">download</th> <tr><td align="left">MaskDINO | <a href="configs/coco/panoptic-segmentation/maskdino_R50_bs16_50ep_3s_dowsample1_2048.yaml">config</a></td> <td align="center">R50</td> <td align="center">50</td> <td align="center">53.0</td> <td align="center">48.8</td> <td align="center">44.3</td> <td align="center">60.6</td> <td align="center"><a href="https://github.com/IDEA-Research/detrex-storage/releases/download/maskdino-v0.1.0/maskdino_r50_50ep_300q_hid2048_3sd1_panoptic_pq53.0.pth">model</a></td> <tr><td align="left">MaskDINO | <a href="configs/coco/panoptic-segmentation/swin/maskdino_R50_bs16_50ep_4s_dowsample1_2048.yaml">config</a></td> <td align="center">Swin-L (IN21k)</td> <td align="center">50</td> <td align="center">58.3</td> <td align="center">50.6</td> <td align="center">56.2</td> <td align="center">67.5</td> <td align="center"><a href="https://github.com/IDEA-Research/detrex-storage/releases/download/maskdino-v0.1.0/maskdino_swinl_50ep_300q_hid2048_3sd1_panoptic_58.3pq.pth">model</a></td> </tr> <tr><td align="left">MaskDINO+O365 data+1.2 x larger image</td> <td align="center">Swin-L (IN21k)</td> <td align="center">20</td> <td align="center">59.4</td> <td align="center">53.0</td> <td align="center">57.7</td> <td align="center">67.3</td> <td align="center">To Release</td> </tr> </tbody></table>Semantic Segmentation
We use hidden dimension 1024
and 100 queries for semantic segmentation.
You can also find all these models here.
All models were trained with 4 NVIDIA A100 GPUs (ResNet-50 based models) or 8 NVIDIA A100 GPUs (Swin-L based models).
We will release more pretrained models in the future.
Getting Started
In the above tables, the "Name" column contains a link config_path
to the config file, and the corresponding model checkpoints
can be downloaded from the link in model
.
If your dataset files are not under this repo, you need to add export DETECTRON2_DATASETS=/path/to/your/data
or use Symbolic Link ln -s
to link the dataset into this repo before the
following command first.
Evalaluate our pretrained models
- You can download our pretrained models and evaluate them with the following commands.
for example, to reproduce our instance segmentation result, you can copy the config path from the table, download the pretrained checkpoint intopython train_net.py --eval-only --num-gpus 8 --config-file config_path MODEL.WEIGHTS /path/to/checkpoint_file
/path/to/checkpoint_file
, and run
which can reproduce the model.python train_net.py --eval-only --num-gpus 8 --config-file configs/coco/instance-segmentation/maskdino_R50_bs16_50ep_3s_dowsample1_2048.yaml MODEL.WEIGHTS /path/to/checkpoint_file
Train MaskDINO to reproduce results
- Use the above command without
eval-only
will train the model. For Swin backbones, you need to specify the path of the pretrained backbones withMODEL.WEIGHTS /path/to/pretrained_checkpoint
python train_net.py --num-gpus 8 --config-file config_path MODEL.WEIGHTS /path/to/checkpoint_file
- For ResNet-50 models, training on 8 GPU requires around
15G
memory on each GPU and3
days training for 50 epochs. - For Swin-L models, training on 8 gpu required memory
60G
on each GPU. If your gpu do not have enough memory, you may also train with 16 GPUs with distributed training on two nodes. - We use total batch size 16 for all our models. If train on 1 GPU, you need to figure out learning rate and batch size by yourself
python train_net.py --num-gpus 1 --config-file config_path SOLVER.IMS_PER_BATCH SET_TO_SOME_REASONABLE_VALUE SOLVER.BASE_LR SET_TO_SOME_REASONABLE_VALUE
You can also refer to Getting Started with Detectron2 for full usage.
More Usage
Mask-enhanced box initialization
We provide 2 ways to convert predicted masks to boxes to initialize decoder boxes. You can set as follows
MODEL.MaskDINO.INITIALIZE_BOX_TYPE: no
not using mask enhanced box initializationMODEL.MaskDINO.INITIALIZE_BOX_TYPE: mask2box
a fast conversion wayMODEL.MaskDINO.INITIALIZE_BOX_TYPE: bitmask
provided conversion from detectron2, slower but more accurate conversion.
These two conversion ways do not affect the final performance much, you can choose either way.
In addition, if you already train a model for 50 epochs without mask-enhance box initialization, you can plug in this method and simply finetune the model in the last few epochs (i.e., load from 32K iteration trained model and finetune it). This way can also achieve similar performance compared with training from scratch, but more flexible.
Model components
MaskDINO consists of three components: a backbone, a pixel decoder and a Transformer decoder. You can easily replace each of these three components with your own implementation.
-
backbone: Define and register your backbone under
maskdino/modeling/backbone
. You can follow the Swin Transformer as an example. -
pixel decoder: pixel decoder is actually the multi-scale encoder in DINO and Deformable DETR, we follow mask2former to call it pixel decoder. It is in
maskdino/modeling/pixel_decoder
, you can change your multi-scale encoder. The returned values includemask_features
is the per-pixel embeddings with resolution 1/4 of the original image, obtained by fusing backbone 1/4 features and multi-scale encoder encoded 1/8 features. This is used to produce binary masks.multi_scale_features
, which is the multi-scale inputs to the Transformer decoder. For ResNet-50 models with 4 scales, we use resolution 1/32, 1/16, and 1/8 but you can use arbitrary resolutions here, and follow DINO to additionally downsample 1/32 to get a 4th scale with 1/64 resolution. For 5-scale models with SwinL, we additional use 1/4 resolution features as in DINO.
-
transformer decoder: it mainly follows DINO decoder to do detection and segmentation tasks. It is defined in
maskdino/modeling/transformer_decoder
.
LICNESE
Mask DINO is released under the Apache 2.0 license. Please see the LICENSE file for more information.
Copyright (c) IDEA. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
<a name="CitingMaskDINO"></a>Citing Mask DINO
If you find our work helpful for your research, please consider citing the following BibTeX entry.
@misc{li2022mask,
title={Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation},
author={Feng Li and Hao Zhang and Huaizhe xu and Shilong Liu and Lei Zhang and Lionel M. Ni and Heung-Yeung Shum},
year={2022},
eprint={2206.02777},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
If you find the code useful, please also consider the following BibTeX entry.
@misc{zhang2022dino,
title={DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection},
author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel M. Ni and Heung-Yeung Shum},
year={2022},
eprint={2203.03605},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{li2022dn,
title={Dn-detr: Accelerate detr training by introducing query denoising},
author={Li, Feng and Zhang, Hao and Liu, Shilong and Guo, Jian and Ni, Lionel M and Zhang, Lei},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13619--13627},
year={2022}
}
@inproceedings{
liu2022dabdetr,
title={{DAB}-{DETR}: Dynamic Anchor Boxes are Better Queries for {DETR}},
author={Shilong Liu and Feng Li and Hao Zhang and Xiao Yang and Xianbiao Qi and Hang Su and Jun Zhu and Lei Zhang},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=oMI9PjOb9Jl}
}
Acknowledgement
Many thanks to these excellent opensource projects