The training speed is measure with s/iter. A summary can be found in the Model Zoo page. Statistics; Model Architecture Summary; Text Detection Models; the only last thing to check is if the models config points MMOCR to the correct dataset path. Then you can start training with the command: You can find full training instructions, explanations and useful training configs in Training. You can find examples in Log Analysis. WebOpenMMLab Model Deployment Framework. Update News | If you use dist_train.sh to launch training jobs, you can set the port in commands. MSRA styles: Corresponding to MSRA weights, including ResNet50_Caffe and ResNet101_Caffe. These models serve as strong pre-trained models for downstream tasks for convenience. Please refer to FAQ for frequently asked questions. Please refer to CentripetalNet for details. Copyright 2018-2021, OpenMMLab. All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo, caffe-style pretrained backbones are converted from the newly released model from detectron2. This project is released under the Apache 2.0 license. Benchmark and model zoo. These models serve as strong pre-trained models for downstream tasks for convenience. FileClient (backend = None, prefix = None, ** kwargs) [] . For Mask R-CNN, we exclude the time of RLE encoding in post-processing. MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. Caffe2 styles: Currently only contains ResNext101_32x8d. Documentation | The img_norm_cfg is dict(mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False). when using 8 gpus for distributed data parallel You signed in with another tab or window. Model Zoo; Data Preparation. License. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. A general file client to access files in Work fast with our official CLI. Copyright 2018-2022, OpenMMLab. Abstract class of storage backends. You can use the following commands to infer a dataset. See tutorial. You may find their preparation steps in these sections: Detection Datasets, Recognition Datasets, KIE Datasets and NER Datasets. Please refer to Install Guide for more detailed instruction. Supported algorithms: MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. If you use launch training jobs with Slurm, you need to modify the config files (usually the 6th line from the bottom in config files) to set different communication ports. A summary can be found in the Model Zoo page. Please refer to Dynamic R-CNN for details. Supported algorithms: Classification. 1: Inference and train with existing models and standard datasets; 2: Train with customized datasets; 3: Train with customized models and standard datasets; Tutorials. Suppose now you have finished the training of DBNet and the latest model has been saved in dbnet/latest.pth. Revision 31c84958. MMDetection Model Zoo Pascal VOCCOCOCityscapesLVIS Results and models are available in the model zoo. Webfileio class mmcv.fileio. For Mask R-CNN, we exclude the time of RLE encoding in post-processing. The script benchmarkes the model with 2000 images and calculates the average time ignoring first 5 times. Please refer to Guided Anchoring for details. WebModel Zoo. Results and models are available in the model zoo. Please read getting_started for the basic usage of MMDeploy. It is common to initialize from backbone models pre-trained on ImageNet classification task. MMRotate: OpenMMLab rotated object detection toolbox and benchmark. TorchVision: Corresponding to Python 3.6+ PyTorch 1.3+ CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible) GCC 5+ MMCV It is common to initialize from backbone models pre-trained on ImageNet classification task. (Please change the data_root firstly.). The img_norm_cfg is dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True). Web# Get the Flops of a model > mim run mmcls get_flops resnet101_b16x8_cifar10.py # Publish a model > mim run mmcls publish_model input.pth output.pth # Train models on a slurm HPC with one GPU > srun -p partition --gres=gpu:1 mim run mmcls train \ resnet101_b16x8_cifar10.py --work-dir tmp # Test models on a slurm HPC with one GPU, Please refer to Efficientnet for details. MMHuman3D . MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. Contribute to open-mmlab/mmdeploy development by creating an account on GitHub. What's New. ], to_rgb=True). If nothing happens, download GitHub Desktop and try again. Please We also provide the checkpoint and training log for reference. MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. The figure above is contributed by RangeKing@GitHub, thank you very much! The img_norm_cfg is dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True). WebAll pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo, caffe-style pretrained backbones are converted from the newly released model from detectron2. It is usually used for resuming the training process that is interrupted accidentally. Copyright 2018-2021, OpenMMLab. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods. More demo and full instructions can be found in Demo. Check out our installation guide for full steps. . MMTracking . --work-dir ${WORK_DIR}: Override the working directory specified in the config file. Webtrain, val and test: The config s to build dataset instances for model training, validation and testing by using build and registry mechanism.. samples_per_gpu: How many samples per batch and per gpu to load during model training, and the batch_size of training is equal to samples_per_gpu times gpu number, e.g. The lower, the better. (, [Enhancement] Install Optimizer by setuptools (, Support setup on environment with no PyTorch (, Multiple inference backends are available, Efficient and scalable C/C++ SDK Framework. Please refer to data preparation for dataset preparation. Architectures. For fair comparison with other codebases, we report the GPU memory as the maximum value of torch.cuda.max_memory_allocated() for all 8 GPUs. It is usually used for resuming the training process that is interrupted accidentally. The inference speed is measured with fps (img/s) on a single GPU, the higher, the better. Learn more. load-from only loads the model weights and the training epoch starts from 0. It is a part of the OpenMMLab project. We also train Faster R-CNN and Mask R-CNN using ResNet-50 and RegNetX-3.2G with multi-scale training and longer schedules. We compare mmdetection with Detectron2 in terms of speed and performance. According to img_norm_cfg and source of weight, we can divide all the ImageNet pre-trained model weights into some cases: TorchVision: Corresponding to torchvision weight, including ResNet50, ResNet101. We compare the training speed of Mask R-CNN with some other popular frameworks (The data is copied from detectron2). Tutorial 1: Learn about Configs; Tutorial 2: Customize Datasets; Tutorial 3: Customize Data Pipelines; Tutorial 4: Customize Models Please refer to Weight Standardization for details. Please refer to Group Normalization for details. Benchmark and Model zoo. MMEditing . MMRotate depends on PyTorch, MMCV and MMDetection. All pre-trained model links can be found at open_mmlab.According to img_norm_cfg and source of weight, we can divide all the ImageNet pre-trained model weights into some cases:. WebA summary can be found in the Model Zoo page. According to img_norm_cfg and source of weight, we can divide all the ImageNet pre-trained model weights into some cases: TorchVision: Corresponding to torchvision weight, including ResNet50, ResNet101. Other styles: E.g SSD which corresponds to img_norm_cfg is dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) and YOLOv3 which corresponds to img_norm_cfg is dict(mean=[0, 0, 0], std=[255., 255., 255. load-from only loads the model weights and the training epoch starts from 0. We provide benchmark.py to benchmark the inference latency. Please refer to Deformable DETR for details. Note that this value is usually less than what nvidia-smi shows. you need to specify different ports (29500 by default) for each job to avoid communication conflict. WebMS means multiple scale image split.. RR means random rotation.. Hou, Liping and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi and. 1: Inference and train with existing models and standard datasets, 3: Train with customized models and standard datasets, Tutorial 8: Pytorch to ONNX (Experimental), Tutorial 9: ONNX to TensorRT (Experimental), mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py, CARAFE: Content-Aware ReAssembly of FEatures. 1 mmdetection3d We decompose the rotated object detection framework into different components, WebModel Zoo. To be consistent with Detectron2, we report the pure inference speed (without the time of data loading). We compare mmdetection with Detectron2 in terms of speed and performance. MMGeneration . For fair comparison with other codebases, we report the GPU memory as the maximum value of torch.cuda.max_memory_allocated() for all 8 GPUs. MMFlow . All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo, caffe-style pretrained backbones are converted from the newly released model from detectron2. Please refer to Group Normalization for details. Work fast with our official CLI. than the results tested on our server due to differences of hardwares. WebWelcome to MMYOLOs documentation! Get Started. For fair comparison with other codebases, we report the GPU memory as the maximum value of torch.cuda.max_memory_allocated() for all 8 GPUs. The supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible. We recommend you upgrade to MMOCR 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. WebContribute to tianweiy/CenterPoint development by creating an account on GitHub. Please refer to data_preparation.md to prepare the data. We compare the training speed of Mask R-CNN with some other popular frameworks (The data is copied from detectron2). The above models are trained with 1 * 1080Ti/2080Ti and inferred with 1 * 2080Ti. You can change the test set path in the data_root to the val set or trainval set for the offline evaluation. upate opencv that enables video build option (, add stale workflow to check issues and PRs (, [Enhancement] add mmaction.yml for test (, [FIX] Fix csharp net48 and batch inference (, [Enhancement] Add pip source in dockerfile for, Reformat multi-line logs and docstrings (, [Feature] Add option to fuse transform. The detailed table of the commonly used backbone models in MMDetection is listed below : Please refer to Faster R-CNN for details. We would like to sincerely thank the following teams for their contributions to MMDeploy: If you find this project useful in your research, please consider citing: This project is released under the Apache 2.0 license. It is a part of the OpenMMLab project. The latency of all models in our model zoo is benchmarked without setting fuse-conv-bn, you can get a lower latency by setting it. It is usually used for finetuning. LiDAR-Based 3D Detection; Vision-Based 3D Detection; LiDAR-Based 3D Semantic Segmentation; Datasets. If you have just multiple machines connected with ethernet, you can refer to It is usually used for resuming the training process that is interrupted accidentally. get() reads the file as a byte stream and get_text() reads the file as texts. If nothing happens, download Xcode and try again. Pose Model Preparation: The pre-trained pose estimation model can be downloaded from model zoo.Take macaque model as an example: We provide benchmark.py to benchmark the inference latency. For mmdetection, we benchmark with mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py, which should have the same setting with mask_rcnn_R_50_FPN_noaug_1x.yaml of detectron2. 2: Train with customized datasets; Supported Tasks. This project is released under the Apache 2.0 license. Caffe2 styles: Currently only contains ResNext101_32x8d. Benchmark and Model Zoo; Model Zoo Statistics; Quick Run. MMYOLO: OpenMMLab YOLO series toolbox and benchmark; Changelog. We provide a toy dataset under tests/data on which you can get a sense of training before the academic dataset is prepared. Once you have prepared required academic dataset following our instruction, the only last thing to check is if the models config points MMOCR to the correct dataset path. MMPose . We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. MMDeploy is an open-source deep learning model deployment toolset. Please refer to Deformable DETR for details. Usually it is slow if you do not have high speed networking like InfiniBand. MMOCR supports numerous datasets which are classified by the type of their corresponding tasks. BaseStorageBackend [] . Please refer to Rethinking ImageNet Pre-training for details. Web1: Inference and train with existing models and standard datasets. Revision 31c84958. For fair comparison, we install and run both frameworks on the same machine. All pre-trained model links can be found at open_mmlab. We also provide tutoials about: You can find the supported models from here and their performance in the benchmark. The currently supported codebases and models are as follows, and more will be included in the future. Please refer to Generalized Focal Loss for details. Revision a4fe6bb6. WebInstall MMCV without MIM. Inference RotatedRetinaNet on DOTA-1.0 dataset, which can generate compressed files for online submission. Supported algorithms: Rotated RetinaNet-OBB/HBB (ICCV'2017) Rotated FasterRCNN-OBB (TPAMI'2017) Rotated RepPoints-OBB (ICCV'2019) MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. Ongoing Projects | WebWelcome to MMOCRs documentation! You can switch between English and Chinese in the lower-left corner of the layout. to use Codespaces. The master branch works with PyTorch 1.5+. See tutorial. WebLike MMDetection and MMCV, MMDetection3D can also be used as a library to support different projects on top of it. We use the commit id 185c27e(30/4/2020) of detectron. MIM solves such dependencies automatically and makes the installation easier. sign in than the results tested on our server due to differences of hardwares. The model zoo of V1.x has been deprecated. to use Codespaces. You can change the output log interval (defaults: 50) by setting LOG-INTERVAL. We also provide the checkpoint and training log for reference. WebModel Zoo. For fair comparison, we install and run both frameworks on the same machine. WebImageNet Pretrained Models. Web 3. The latency of all models in our model zoo is benchmarked without setting fuse-conv-bn, you can get a lower latency by setting it. Installation | Please refer to changelog.md for details and release history. Object Detection: MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. WebBenchmark and Model Zoo; Quick Run. You can perform end-to-end OCR on our demo image with one simple line of command: Its detection result will be printed out and a new window will pop up with result visualization. ImageNet open_mmlab img_norm_cfg ImageNet . Are you sure you want to create this branch? MMFewShot . Architectures. Baseline (ICLR'2019) Baseline++ (ICLR'2019) MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. All models were trained on coco_2017_train, and tested on the coco_2017_val. Use Git or checkout with SVN using the web URL. Web1: . Learn about Configs with YOLOv5 Supported methods: FlowNet (ICCV'2015) FlowNet2 (CVPR'2017) PWC-Net (CVPR'2018) MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. ~60 FPS on Waymo Open Dataset.There is also a nice onnx conversion repo by CarkusL. OpenMMLab Rotated Object Detection Toolbox and Benchmark. The img_norm_cfg is dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False). Revision bc1ced4c. Results and models are available in the model zoo. Results and models are available in the README.md of each method's config directory. WebAllows any kind of single-stage model as an RPN in a two-stage model. DARTS(ICLR'2019) DetNAS(NeurIPS'2019) SPOS(ECCV'2020) MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. Linux | macOS | Windows. If nothing happens, download GitHub Desktop and try again. Benchmark and Model Zoo; Model Zoo Statistics; Quick Run. We also train Faster R-CNN and Mask R-CNN using ResNet-50 and RegNetX-3.2G with multi-scale training and longer schedules. We appreciate all contributions to improve MMRotate. We provide a demo script to test a single image, given gt json file. In this guide we will show you some useful commands and familiarize you with MMOCR. We also benchmark some methods on PASCAL VOC, Cityscapes, OpenImages and WIDER FACE. WebImageNet . Web Documentation | Installation | Model Zoo | Update News | Ongoing Projects | Reporting Issues. Use Git or checkout with SVN using the web URL. v0.2.0 was We provide colab tutorial, and other tutorials for: Results and models are available in the README.md of each method's config directory. It is based on PyTorch and MMCV. Pycls: Corresponding to pycls weight, including RegNetX. . Please see get_started.md for the basic usage of MMRotate. WebUsing gt bounding boxes as input. MMRotate: OpenMMLab rotated object detection toolbox and 1: Inference and train with existing models and standard datasets; New Data and Model. The toolbox provides strong baselines and state-of-the-art methods in rotated object detection. Please refer to CONTRIBUTING.md for the contributing guideline. Train a model; Inference with pretrained models; Tutorials. Please refer to Deformable Convolutional Networks for details. You can evaluate its performance on the test set using the hmean-iou metric with the following command: Evaluating any pretrained model accessible online is also allowed: More instructions on testing are available in Testing. MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies. We use the commit id 185c27e(30/4/2020) of detectron. Please refer to CentripetalNet for details. Please refer to Deformable Convolutional Networks for details. pytorchtorch.hubFacebookPyTorch HubAPIPyTorch HubColabPapers With Code18 For mmdetection, we benchmark with mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py, which should have the same setting with mask_rcnn_R_50_FPN_noaug_1x.yaml of detectron2. WebMMDetection3D . WebModel Zoo (by paper) Algorithms; Backbones; Datasets; Techniques; Tutorials. Other styles: E.g SSD which corresponds to img_norm_cfg is dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) and YOLOv3 which corresponds to img_norm_cfg is dict(mean=[0, 0, 0], std=[255., 255., 255. class mmcv.fileio. English | . Reporting Issues. TorchVisiontorchvision ResNet50, ResNet101 WebDifference between resume-from and load-from: resume-from loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. Benchmark and model zoo. Train & Test. Please refer to Generalized Focal Loss for details. We provide analyze_logs.py to get average time of iteration in training. WebPrerequisites. Please refer to Mask Scoring R-CNN for details. Below are quick steps for installation. Object Detection: MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. Web 3. The img_norm_cfg is dict(mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False). WebBenchmark and model zoo. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The detailed table of the commonly used backbone models in MMDetection is listed below : Please refer to Faster R-CNN for details. Then you can launch two jobs with config1.py and config2.py. Please refer to CONTRIBUTING.md for the contributing guideline. If you use this toolbox or benchmark in your research, please cite this project. MMSegmentation . Note that this value is usually less than what nvidia-smi shows. We only use aliyun to maintain the model zoo since MMDetection V2.0. Overview; Get Started; User Guides. v1.0.0rc5 was released in 11/10/2022. All pre-trained model links can be found at open_mmlab. ], to_rgb=True). Model Zoo | show_dir: Directory where painted GT and detection images will be saved--show Determines whether to show painted images, If not specified, it will be set to False--wait-time: The interval of show (s), 0 is block If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, Please refer to Weight Standardization for details. The lower, the better. The script benchmarkes the model with 2000 images and calculates the average time ignoring first 5 times. If you launch with multiple machines simply connected with ethernet, you can simply run following commands: Usually it is slow if you do not have high speed networking like InfiniBand. Dataset Preparation; Exist Data and Model. Please The img_norm_cfg is dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False). Model Zoo. There was a problem preparing your codespace, please try again. The throughput is computed as the average throughput in iterations 100-500 to skip GPU warmup time. Supported algorithms: Neural Architecture Search. WebModel Zoo. Results are obtained with the script benchmark.py which computes the average time on 2000 images. Results and models are available in the model zoo. Please refer to changelog.md for details and release history. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Please refer to Guided Anchoring for details. If you run MMRotate on a cluster managed with slurm, you can use the script slurm_train.sh. We also include the officially reported speed in the parentheses, which is slightly higher Copyright 2020-2030, OpenMMLab. We also include the officially reported speed in the parentheses, which is slightly higher Pycls: Corresponding to pycls weight, including RegNetX. The img_norm_cfg is dict( mean=[103.530, 116.280, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False). NEWS [2021-12-27] We release a multimodal fusion approach for 3D detection MVP. Check out the maintenance plan, changelog, code and documentation of MMOCR 1.0 for more details. . MMRotate is an open-source toolbox for rotated object detection based on PyTorch. We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. WebDescription of all arguments: config: The path of a model config file.. prediction_path: Output result file in pickle format from tools/test.py. To disable this behavior, use --no-validate. A tag already exists with the provided branch name. (This script also supports single machine training.). 3D3D2DMMDetectionbenchmarkMMDetection3DMMDet3DMMDetection3D , 3Dcodebase3DMMDetection3D+3DMVX-NetKITTI MMDetection3Dcodebase, 3Dcodebase MMDetection3DScanNetSUNRGBDKITTInuScenesLyftVoteNet state of the artPartA2-NetPointPillars MMDetection3Ddata pipelinemodel, 3Dcodebasecodebase2DSOTAMMDetection3D MMDetection3DMMDetectionMMCVMMDetectionAPIMMDetectionhookMMCVtrain_detectorMMDetection3D config, MMDetection model zoo300+40+MMDetection3DMMDetection3DMMDetection3DMMDetectionMMDetection3Dclaim, 3DVoteNetSECONDPointPillars8/codebasex, MMDetection3DMMDetectionconfigMMDetectionmodular designMMDetectioncodebaseMMDetection3D MMDetection3DMMDetection detectron2packageMMDetection3D project pip install mmdet3d release MMDetection3Dproject import mmdet3d mmdet3d , MMDetection3DSECOND.PytorchTarget assignNumPyDataloaderMMDetection3DMMDetectionassignerMMDetection3DPyTorchCUDAMMDetection3DcodebasespconvspconvMMDetection3DMMDetection3DMMDetection, MMDetection3D SOTA nuscenesPointPillars + RegNet3.2GF + FPN + FreeAnchor + Test-time augmentationCBGS GT-samplingNDS 65, mAP 57LiDARrelease model zoo , MMDetection3D3Dcodebase//SOTAcommunityfree stylecodebaseforkstarPR, MMDetection3D VoteNet, MVXNet, Part-A2PointPillarsSOTA; MMDetection300+40+3D, MMDetection3D SUN RGB-D, ScanNet, nuScenes, Lyft, KITTI53D, MMDetection3D pip install, MMDetection2D, MMDetectionMMCVGCBlockDCNFPNFocalLossMMDetection3D2D3DgapLossMMDetection3Dworksolid. Learn more. MMdetection3dMMdetection3d3D. Suppose we want to train DBNet on ICDAR 2015, and part of configs/_base_/det_datasets/icdar2015.py looks like the following: You would need to check if data/icdar2015 is right. All backends need to implement two apis: get() and get_text(). We appreciate all contributions to MMDeploy. Please refer to Dynamic R-CNN for details. Results are obtained with the script benchmark.py which computes the average time on 2000 images. resume-from loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. If you want to specify the working directory in the command, you can add an argument --work_dir ${YOUR_WORK_DIR}. The training speed is measure with s/iter. To train a text recognition task with sar method and toy dataset. 1: Inference and train with existing models and standard datasets, 3: Train with customized models and standard datasets, Tutorial 8: Pytorch to ONNX (Experimental), Tutorial 9: ONNX to TensorRT (Experimental), mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py, CARAFE: Content-Aware ReAssembly of FEatures. PyTorch launch utility. . We provide analyze_logs.py to get average time of iteration in training. MMRotate: OpenMMLab rotated object detection toolbox and benchmark. MSRA styles: Corresponding to MSRA weights, including ResNet50_Caffe and ResNet101_Caffe. WebDifference between resume-from and load-from: resume-from loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. If nothing happens, download Xcode and try again. You can change the output log interval (defaults: 50) by setting LOG-INTERVAL. All models were trained on coco_2017_train, and tested on the coco_2017_val. sign in Benchmark and model zoo Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Train a model; Inference with pretrained models; Tutorials. You can find examples in Log Analysis. Please refer to Cascade R-CNN for details. Allows any kind of single-stage model as an RPN in a two-stage model. For example, to train a text recognition task with seg method and toy dataset. [2021-12-27] A TensorRT implementation (by Wang Hao) of CenterPoint-PointPillar is available at URL. MMGeneration is a powerful toolkit for generative models, especially for GANs now. Are you sure you want to create this branch? The inference speed is measured with fps (img/s) on a single GPU, the higher, the better. You are reading the documentation for MMOCR 0.x, which will soon be deprecated by the end of 2022. WebMMYOLO Model Zoo The img_norm_cfg is dict( mean=[103.530, 116.280, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False). Difference between resume-from and load-from: Please refer to Efficientnet for details. The model zoo of V1.x has been deprecated. A tag already exists with the provided branch name. We only use aliyun to maintain the model zoo since MMDetection V2.0. 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