Mmdetection Versions Save

OpenMMLab Detection Toolbox and Benchmark

v3.3.0

3 months ago

MM Grounding DINO

An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness has led to its widespread adoption as a mainstream architecture for various downstream applications. However, despite its significance, the original Grounding-DINO model lacks comprehensive public technical details due to the unavailability of its training code. To bridge this gap, we present MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline, which is built with the MMDetection toolbox. It adopts abundant vision datasets for pre-training and various detection and grounding datasets for fine-tuning. We give a comprehensive analysis of each reported result and detailed settings for reproduction. The extensive experiments on the benchmarks mentioned demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny baseline. We release all our models to the research community.

Detail: https://github.com/open-mmlab/mmdetection/tree/main/configs/mm_grounding_dino

v3.2.0

6 months ago

Highlight

v3.2.0 was released in 12/10/2023:

1. Detection Transformer SOTA Model Collection (1) Supported four updated and stronger SOTA Transformer models: DDQ, CO-DETR, AlignDETR, and H-DINO. (2) Based on CO-DETR, MMDet released a model with a COCO performance of 64.1 mAP. (3) Algorithms such as DINO support AMP/Checkpoint/FrozenBN, which can effectively reduce memory usage.

2. Comprehensive Performance Comparison between CNN and Transformer RF100 consists of a dataset collection of 100 real-world datasets, including 7 domains. It can be used to assess the performance differences of Transformer models like DINO and CNN-based algorithms under different scenarios and data volumes. Users can utilize this benchmark to quickly evaluate the robustness of their algorithms in various scenarios.

3. Support for GLIP and Grounding DINO fine-tuning, the only algorithm library that supports Grounding DINO fine-tuning The Grounding DINO algorithm in MMDet is the only library that supports fine-tuning. Its performance is one point higher than the official version, and of course, GLIP also outperforms the official version. We also provide a detailed process for training and evaluating Grounding DINO on custom datasets. Everyone is welcome to give it a try.

Model Backbone Style COCO mAP Official COCO mAP
Grounding DINO-T Swin-T Zero-shot 48.5 48.4
Grounding DINO-T Swin-T Finetune 58.1(+0.9) 57.2
Grounding DINO-B Swin-B Zero-shot 56.9 56.7
Grounding DINO-B Swin-B Finetune 59.7
Grounding DINO-R50 R50 Scratch 48.9(+0.8) 48.1

4. Support for the open-vocabulary detection algorithm Detic and multi-dataset joint training. 5. Training detection models using FSDP and DeepSpeed.

ID AMP GC of Backbone GC of Encoder FSDP Peak Mem (GB) Iter Time (s)
1 49 (A100) 0.9
2 √ 39 (A100) 1.2
3 √ 33 (A100) 1.1
4 √ √ 25 (A100) 1.3
5 √ √ 18 2.2
6 √ √ √ 13 1.6
7 √ √ √ 14 2.9
8 √ √ √ √ 8.5 2.4

6. Support for the V3Det dataset, a large-scale detection dataset with over 13,000 categories.

亮点

v3.2.0 版本已经在 2023.10.12 发布：

1. 检测 Transformer SOTA 模型大合集 (1) 支持了 DDQ、CO-DETR、AlignDETR 和 H-DINO 4 个更新更强的 SOTA Transformer 模型 (2) 基于 CO-DETR, MMDet 中发布了 COCO 性能为 64.1 mAP 的模型 (3) DINO 等算法支持 AMP/Checkpoint/FrozenBN，可以有效降低显存

2. 提供了全面的 CNN 和 Transformer 的性能对比 RF100 是由 100 个现实收集的数据集组成，包括 7 个域，可以验证 DINO 等 Transformer 模型和 CNN 类算法在不同场景不同数据量下的性能差异。用户可以用这个 Benchmark 快速验证自己的算法在不同场景下的鲁棒性。

3. 支持了 GLIP 和 Grounding DINO 微调，全网唯一支持 Grounding DINO 微调 MMDet 中的 Grounding DINO 是全网唯一支持微调的算法库，且性能高于官方 1 个点，当然 GLIP 也比官方高。 我们还提供了详细的 Grounding DINO 在自定义数据集上训练评估的流程，欢迎大家试用。

Model Backbone Style COCO mAP Official COCO mAP
Grounding DINO-T Swin-T Zero-shot 48.5 48.4
Grounding DINO-T Swin-T Finetune 58.1(+0.9) 57.2
Grounding DINO-B Swin-B Zero-shot 56.9 56.7
Grounding DINO-B Swin-B Finetune 59.7
Grounding DINO-R50 R50 Scratch 48.9(+0.8) 48.1

4. 支持开放词汇检测算法 Detic 并提供多数据集联合训练可能

5. 轻松使用 FSDP 和 DeepSpeed 训练检测模型

ID AMP GC of Backbone GC of Encoder FSDP Peak Mem (GB) Iter Time (s)
1 49 (A100) 0.9
2 √ 39 (A100) 1.2
3 √ 33 (A100) 1.1
4 √ √ 25 (A100) 1.3
5 √ √ 18 2.2
6 √ √ √ 13 1.6
7 √ √ √ 14 2.9
8 √ √ √ √ 8.5 2.4

6. 支持了 V3Det 1.3w+ 类别的超大词汇检测数据集

v3.1.0

9 months ago

Highlights

  • Supports tracking algorithms including multi-object tracking (MOT) algorithms SORT, DeepSORT, StrongSORT, OCSORT, ByteTrack, QDTrack, and video instance segmentation (VIS) algorithm MaskTrackRCNN, Mask2Former-VIS.
  • Support ViTDet
  • Supports inference and evaluation of multimodal algorithms GLIP and XDecoder, and also supports datasets such as COCO semantic segmentation, COCO Caption, ADE20k general segmentation, and RefCOCO. GLIP fine-tuning will be supported in the future.
  • Provides a gradio demo for image type tasks of MMDetection, making it easy for users to experience.

Exciting Features

GLIP inference and evaluation

s multimodal vision algorithms continue to evolve, MMDetection has also supported such algorithms. This section demonstrates how to use the demo and eval scripts corresponding to multimodal algorithms using the GLIP algorithm and model as the example. Moreover, MMDetection integrated a gradio_demo project, which allows developers to quickly play with all image input tasks in MMDetection on their local devices. Check the document for more details.

Preparation

Please first make sure that you have the correct dependencies installed:

# if source
pip install -r requirements/multimodal.txt

# if wheel
mim install mmdet[multimodal]

MMDetection has already implemented GLIP algorithms and provided the weights, you can download directly from urls:

cd mmdetection
wget https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_a_mmdet-b3654169.pth

Inference

Once the model is successfully downloaded, you can use the demo/image_demo.py script to run the inference.

python demo/image_demo.py demo/demo.jpg glip_tiny_a_mmdet-b3654169.pth --texts bench

Demo result will be similar to this:

If users would like to detect multiple targets, please declare them in the format of xx . xx . after the --texts.

python demo/image_demo.py demo/demo.jpg glip_tiny_a_mmdet-b3654169.pth --texts 'bench . car .'

And the result will be like this one:

You can also use a sentence as the input prompt for the --texts field, for example:

python demo/image_demo.py demo/demo.jpg glip_tiny_a_mmdet-b3654169.pth --texts 'There are a lot of cars here.'

The result will be similar to this:

Evaluation

The GLIP implementation in MMDetection does not have any performance degradation, our benchmark is as follows:

Model official mAP mmdet mAP
glip_A_Swin_T_O365.yaml 42.9 43.0
glip_Swin_T_O365.yaml 44.9 44.9
glip_Swin_L.yaml 51.4 51.3

Users can use the test script we provided to run evaluation as well. Here is a basic example:

# 1 gpu
python tools/test.py configs/glip/glip_atss_swin-t_fpn_dyhead_pretrain_obj365.py glip_tiny_a_mmdet-b3654169.pth

# 8 GPU
./tools/dist_test.sh configs/glip/glip_atss_swin-t_fpn_dyhead_pretrain_obj365.py glip_tiny_a_mmdet-b3654169.pth 8

The result will be similar to this:

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.428
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.594
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.466
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.300
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.477
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.534
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.634
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.634
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.634
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.473
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.690
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.789

XDecoder

Installation

# if source
pip install -r requirements/multimodal.txt

# if wheel
mim install mmdet[multimodal]

How to use it?

For convenience, you can download the weights to the mmdetection root dir

wget https://download.openmmlab.com/mmdetection/v3.0/xdecoder/xdecoder_focalt_last_novg.pt
wget https://download.openmmlab.com/mmdetection/v3.0/xdecoder/xdecoder_focalt_best_openseg.pt

The above two weights are directly copied from the official website without any modification. The specific source is https://github.com/microsoft/X-Decoder

For convenience of demonstration, please download the folder and place it in the root directory of mmdetection.

(1) Open Vocabulary Semantic Segmentation

cd projects/XDecoder
python demo.py ../../images/animals.png configs/xdecoder-tiny_zeroshot_open-vocab-semseg_coco.py --weights ../../xdecoder_focalt_last_novg.pt --texts zebra.giraffe

(2) Open Vocabulary Instance Segmentation

cd projects/XDecoder
python demo.py ../../images/owls.jpeg configs/xdecoder-tiny_zeroshot_open-vocab-instance_coco.py --weights ../../xdecoder_focalt_last_novg.pt --texts owl

(3) Open Vocabulary Panoptic Segmentation

cd projects/XDecoder
python demo.py ../../images/street.jpg configs/xdecoder-tiny_zeroshot_open-vocab-panoptic_coco.py --weights ../../xdecoder_focalt_last_novg.pt  --text car.person --stuff-text tree.sky

(4) Referring Expression Segmentation

cd projects/XDecoder
python demo.py ../../images/fruit.jpg configs/xdecoder-tiny_zeroshot_open-vocab-ref-seg_refcocog.py --weights ../../xdecoder_focalt_last_novg.pt  --text "The larger watermelon. The front white flower. White tea pot."

(5) Image Caption

cd projects/XDecoder
python demo.py ../../images/penguin.jpeg configs/xdecoder-tiny_zeroshot_caption_coco2014.py --weights ../../xdecoder_focalt_last_novg.pt

(6) Referring Expression Image Caption

cd projects/XDecoder
python demo.py ../../images/fruit.jpg configs/xdecoder-tiny_zeroshot_ref-caption.py --weights ../../xdecoder_focalt_last_novg.pt --text 'White tea pot'

(7) Text Image Region Retrieval

cd projects/XDecoder
python demo.py ../../images/coco configs/xdecoder-tiny_zeroshot_text-image-retrieval.py --weights ../../xdecoder_focalt_last_novg.pt --text 'pizza on the plate'

The image that best matches the given text is ../../images/coco/000.jpg and probability is 0.998

We have also prepared a gradio program in the projects/gradio_demo directory, which you can run interactively all the inference supported by mmdetection in your browser.

Models and results

Semantic segmentation on ADE20K

Prepare your dataset according to the docs.

Test Command

Since semantic segmentation is a pixel-level task, we don't need to use a threshold to filter out low-confidence predictions. So we set model.test_cfg.use_thr_for_mc=False in the test command.

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-semseg_ade20k.py xdecoder_focalt_best_openseg.pt 8 --cfg-options model.test_cfg.use_thr_for_mc=False
Model mIoU mIOU(official) Config
xdecoder_focalt_best_openseg.pt 25.24 25.13 config

Instance segmentation on ADE20K

Prepare your dataset according to the docs.

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-instance_ade20k.py xdecoder_focalt_best_openseg.pt 8
Model mIoU mIOU(official) Config
xdecoder_focalt_best_openseg.pt 10.1 10.1 config

Panoptic segmentation on ADE20K

Prepare your dataset according to the docs.

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-panoptic_ade20k.py xdecoder_focalt_best_openseg.pt 8
Model mIoU mIOU(official) Config
xdecoder_focalt_best_openseg.pt 19.11 18.97 config

Semantic segmentation on COCO2017

Prepare your dataset according to the docs of (2) use panoptic dataset part.

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-semseg_coco.py xdecoder_focalt_last_novg.pt 8 --cfg-options model.test_cfg.use_thr_for_mc=False
Model mIOU mIOU(official) Config
xdecoder-tiny_zeroshot_open-vocab-semseg_coco 62.1 62.1 config

Instance segmentation on COCO2017

Prepare your dataset according to the docs.

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-instance_coco.py xdecoder_focalt_last_novg.pt 8
Model Mask mAP Mask mAP(official) Config
xdecoder-tiny_zeroshot_open-vocab-instance_coco 39.8 39.7 config

Panoptic segmentation on COCO2017

Prepare your dataset according to the docs.

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-panoptic_coco.py xdecoder_focalt_last_novg.pt 8
Model PQ PQ(official) Config
xdecoder-tiny_zeroshot_open-vocab-panoptic_coco 51.42 51.16 config

Referring segmentation on RefCOCO

Prepare your dataset according to the docs.

./tools/dist_test.sh  projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-ref-seg_refcocog.py xdecoder_focalt_last_novg.pt 8  --cfg-options test_dataloader.dataset.split='val'
Model text mode cIoU cIOU(official) Config
xdecoder_focalt_last_novg.pt select first 58.8415 57.85 config
xdecoder_focalt_last_novg.pt original 60.0321 - config
xdecoder_focalt_last_novg.pt concat 60.3551 - config

Note:

  1. If you set the scale of Resize to (1024, 512), the result will be 57.69.
  2. text mode is the RefCoCoDataset parameter in MMDetection, it determines the texts loaded to the data list. It can be set to select_first, original, concat and random.
    • select_first: select the first text in the text list as the description to an instance.
    • original: use all texts in the text list as the description to an instance.
    • concat: concatenate all texts in the text list as the description to an instance.
    • random: randomly select one text in the text list as the description to an instance, usually used for training.

Image Caption on COCO2014

Prepare your dataset according to the docs.

Before testing, you need to install jdk 1.8, otherwise it will prompt that java does not exist during the evaluation process

./tools/dist_test.sh projects/XDecoder/configs/xdecoder-tiny_zeroshot_caption_coco2014.py xdecoder_focalt_last_novg.pt 8
Model BLEU-4 CIDER Config
xdecoder-tiny_zeroshot_caption_coco2014 35.26 116.81 config

Gradio Demo

Please refer to https://github.com/open-mmlab/mmdetection/blob/dev-3.x/projects/gradio_demo/README.md for details.

Contributors

A total of 30 developers contributed to this release.

Thanks @jjjkkkjjj @lovelykite, @minato-ellie, @freepoet, @wufan-tb, @yalibian, @keyakiluo, @gihanjayatilaka, @i-aki-y, @xin-li-67, @RangeKing, @JingweiZhang12, @MambaWong, @lucianovk, @tall-josh, @xiuqhou, @jamiechoi1995, @YQisme, @yechenzhi, @bjzhb666, @xiexinch, @jamiechoi1995, @yarkable, @Renzhihan, @nijkah, @amaizr, @Lum1104, @zwhus, @Czm369, @hhaAndroid

v3.0.0

1 year ago

v3.0.0 (6/4/2023)

We have released the official version of MMDetection v3.0.0

Highlights

New Features

  • File I/O migration and reconstruction (#9709)
  • Release DINO Swin-L 36e model (#9927)

Bug Fixes

  • Fix benchmark script (#9865)
  • Fix the crop method of PolygonMasks (#9858)
  • Fix Albu augmentation with the mask shape (#9918)
  • Fix RTMDetIns prior generator device error (#9964)
  • Fix img_shape in data pipeline (#9966)
  • Fix cityscapes import error (#9984)
  • Fix solov2_r50_fpn_ms-3x_coco.py config error (#10030)
  • Fix Conditional DETR AP and Log (#9889)
  • Fix accepting an unexpected argument local-rank in PyTorch 2.0 (#10050)
  • Fix common/ms_3x_coco-instance.py config error (#10056)
  • Fix compute flops error (#10051)
  • Delete data_root in CocoOccludedSeparatedMetric to fix bug (#9969)
  • Unifying metafile.yml (#9849)

Improvements

  • Added BoxInst r101 config (#9967)
  • Added config migration guide (#9960)
  • Added more social networking links (#10021)
  • Added RTMDet config introduce (#10042)
  • Added visualization docs (#9938, #10058)
  • Refined data_prepare docs (#9935)
  • Added support for setting the cache_size_limit parameter of dynamo in PyTorch 2.0 (#10054)
  • Updated coco_metric.py (#10033)
  • Update type hint (#10040)

Contributors

A total of 19 developers contributed to this release.

Thanks @IRONICBo, @vansin, @RangeKing, @Ghlerrix, @okotaku, @JosonChan1998, @zgzhengSEU, @bobo0810, @yechenzhi, @Zheng-LinXiao, @LYMDLUT, @yarkable, @xiejiajiannb, @chhluo, @BIGWangYuDong, @RangiLyu, @zwhus, @hhaAndroid, @ZwwWayne

v2.28.2

1 year ago

New Features and Improvements

  • Add Twitter, Discord, Medium and YouTube link (#9774)
  • Update customize_runtime.md (#9797)

Bug Fixes

  • Fix WIDERFace SSD loss for Nan problem (#9734)
  • Fix missing API documentation in Readthedoc (#9729)
  • Fix the configuration file and log path of CenterNet (#9791)

New Contributors

Contributors

A total of 4 developers contributed to this release. Thanks @co63oc, @Ginray, @vansin, @RangiLyu

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.28.1...v2.28.2

v3.0.0rc6

1 year ago

Highlights

New Features

  • Support Boxinst (#9525)
  • Support Objects365 Dataset (#9600)
  • Support ConvNeXt-V2 in Projects (#9619)
  • Support DiffusionDet in Projects (#9639, #9768)
  • Support Detic inference in Projects (#9645)
  • Support EfficientDet inference in Projects (#9645)
  • Support Separated and Occluded COCO metric (#9710)
  • Support auto import modules from registry (#9143)
  • Refactor DETR series and support Conditional-DETR, DAB-DETR and DINO (#9646)
  • Support DetInferencer for inference (#9561)
  • Support Test Time Augmentation (#9452)
  • Support calculating FLOPs of detectors (#9777)

Bug Fixes

  • Fix deprecating old type alias due to new version of numpy (#9625, #9537)
  • Fix VOC metrics (#9784)
  • Fix the wrong link of RTMDet-x log (#9549)
  • Fix RTMDet link in README (#9575)
  • Fix MMDet get flops error (#9589)
  • Fix use_depthwise in RTMDet (#9624)
  • Fix albumentations augmentation post process with masks (#9551)
  • Fix DETR series Unit Test (#9647)
  • Fix LoadPanopticAnnotations bug (#9703)
  • Fix isort CI (#9680)
  • Fix amp pooling overflow (#9670)
  • Fix docstring about noise in DINO (#9747)
  • Fix potential bug in MultiImageMixDataset (#9764)

Improvements

  • Replace NumPy transpose with PyTorch permute to speed-up (#9762)
  • Deprecate sklearn (#9725)
  • Add RTMDet-Ins deployment guide (#9823)
  • Update RTMDet config and README (#9603)
  • Replace the models used in the tutorial document with RTMDet (#9843)
  • Adjust the minimum supported python version to 3.7 (#9602)
  • Support modifying palette through configuration (#9445)
  • Update README document in Project (#9599)
  • Replace github with gitee in .pre-commit-config-zh-cn.yaml file (#9586)
  • Use official isort in .pre-commit-config.yaml file (#9701)
  • Change MMCV minimum version to 2.0.0rc4 for dev-3.x (#9695)
  • Add Chinese version of single_stage_as_rpn.md and test_results_submission.md (#9434)
  • Add OpenDataLab download link (#9605, #9738)
  • Add type hints of several layers (#9346)
  • Add typehint for DarknetBottleneck (#9591)
  • Add dockerfile that is easier to use in China (#9659)
  • Add twitter, discord, medium, and youtube link (#9775)
  • Prepare for merging refactor-detr (#9656)
  • Add metafile to ConditionalDETR, DABDETR and DINO (#9715)
  • Support to modify non_blocking parameters (#9723)
  • Comment repeater visualizer register (#9740)
  • Update user guide: finetune.md and inference.md (#9578)

New Contributors

Contributors

A total of 27 developers contributed to this release.

Thanks @JosonChan1998, @RangeKing, @NoFish-528, @likyoo, @Xiangxu-0103, @137208, @PeterH0323, @tianleiSHI, @wufan-tb, @lyviva, @zwhus, @jshilong, @Li-Qingyun, @sanbuphy, @zylo117, @triple-Mu, @KeiChiTse, @LYMDLUT, @nijkah, @chg0901, @DanShouzhu, @zytx121, @vansin, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v3.0.0rc5...v3.0.0rc6

v2.28.1

1 year ago

Bug Fixes

  • Enable to set float mlp_ratio in SwinTransformer (#8670)
  • Fix import error that causes training failure (#9694)
  • Fix isort version in lint (#9685)
  • Fix init_cfg of YOLOF (#8243)

Contributors

A total of 4 developers contributed to this release. Thanks @triple-Mu, @i-aki-y, @twmht, @RangiLyu

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.28.0...v2.28.1

v2.28.0

1 year ago

Highlights

  • Support Objects365 Dataset and Separated and Occluded COCO metric
  • Support acceleration of RetinaNet and SSD on Ascend
  • Deprecate the support of Python 3.6

New Features and Improvements

  • Support Objects365 Dataset (#7525)
  • Support Separated and Occluded COCO metric (#9574)
  • Support acceleration of RetinaNet and SSD on Ascend with documentation (#9648, #9614)
  • Added missing - to --format-only in documentation.

Deprecations

  • Upgrade the minimum Python version to 3.7, the support of Python 3.6 is no longer guaranteed (#9604)

Bug Fixes

  • Fix validation loss logging by (#9663)
  • Fix inconsistent float precision between mmdet and mmcv (#9570)
  • Fix argument name for fp32 in DeformableDETRHead (#9607)
  • Fix typo of all config file path in Metafile.yml (#9627)

Contributors

A total of 11 developers contributed to this release. Thanks @eantono, @akstt, @@lpizzinidev, @RangiLyu, @kbumsik, @tianleiSHI, @nijkah, @BIGWangYuDong, @wangjiangben-hw, @@jamiechoi1995, @ZwwWayne

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.27.0...v2.28.0

v2.27.0

1 year ago

Highlights

Bug Fixes

  • Fix deadlock issue related with MMDetWandbHook (#9476)

Improvements

  • Add minimum GitHub token permissions for workflows (#8928)
  • Delete compatible code for parrots in roi extractor (#9503)
  • Deprecate np.bool Type Alias (#9498)
  • Replace numpy transpose with torch permute to speed-up data pre-processing (#9533)

Documents

  • Fix typo in docs/zh_cn/tutorials/config.md (#9416)
  • Fix Faster RCNN FP16 config link in README (#9366)

Contributors

A total of 12 developers contributed to this release. Thanks @Min-Sheng, @gasvn, @lzyhha, @jbwang1997, @zachcoleman, @chenyuwang814, @MilkClouds, @Fizzez, @boahc077, @apatsekin, @zytx121, @DonggeunYu

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.26.0...v2.27.0

v3.0.0rc5

1 year ago

Highlights

New Features

Bug Fixes

  • Fix CondInst predict error when batch_size is greater than 1 in inference (#9400)
  • Fix the bug of visualization when the dtype of the pipeline output image is not uint8 in browse dataset (#9401)
  • Fix analyze_logs.py to plot mAP and calculate train time correctly (#9409)
  • Fix backward inplace error with PAFPN (#9450)
  • Fix config import links in model converters (#9441)
  • Fix DeformableDETRHead object has no attribute loss_single (#9477)
  • Fix the logic of pseudo bboxes predicted by teacher model in SemiBaseDetector (#9414)
  • Fix demo API in instance segmentation tutorial (#9226)
  • Fix analyze_results (#9380)
  • Fix the error that Readthedocs API cannot be displayed (#9510)

Improvements

  • Remove legacy builder.py (#9479)
  • Make sure the pipeline argument shape is in (width, height) order (#9324)
  • Add .pre-commit-config-zh-cn.yaml file (#9388)
  • Refactor dataset metainfo to lowercase (#9469)
  • Add PyTorch 1.13 checking in CI (#9478)
  • Adjust FocalLoss and QualityFocalLoss to allow different kinds of targets (#9481)
  • Refactor setup.cfg (#9370)
  • Clip saturation value to valid range [0, 1] (#9391)
  • Only keep meta and state_dict when publishing model (#9356)
  • Add segm evaluator in ms-poly_3x_coco_instance config (#9524)
  • Update deployment guide (#9527)
  • Update zh_cn faq.md (#9396)
  • Update get_started (#9480)
  • Update the zh_cn user_guides of useful_tools.md and useful_hooks.md (#9453)
  • Add type hints for bfp and channel_mapper (#9410)
  • Add type hints of several losses (#9397)
  • Add type hints and update docstring for task modules (#9468)

Contributors

A total of 20 developers contributed to this release.

Thanks @liuyanyi, @RangeKing, @lihua199710, @MambaWong, @sanbuphy, @Xiangxu-0103, @twmht, @JunyaoHu, @Chan-Sun, @tianleiSHI, @zytx121, @kitecats, @QJC123654, @JosonChan1998, @lvhan028, @Czm369, @BIGWangYuDong, @RangiLyu, @hhaAndroid, @ZwwWayne

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v3.0.0rc4...v3.0.0rc5