Mmdetection

MMDetection is an open source object detection toolbox based on PyTorch. It is a part mmdetection the OpenMMLab project, mmdetection.

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project. For nuScenes dataset, we also support nuImages dataset. It trains faster than other codebases. The main results are as below. Details can be found in benchmark. We compare the number of samples trained per second the higher, the better.

Mmdetection

Object detection stands as a crucial and ever-evolving field. One of the latest and most notable tools in this domain is MMDetection, an open-source object detection toolbox based on PyTorch. MMDetection is a comprehensive toolbox that provides a wide array of object detection algorithms. It's designed to facilitate research and development in object detection, instance segmentation, and other related areas. It's advisable to review the entire setup process beforehand, as we've identified certain steps that might be tricky or simply not working. The first step in preparing your environment involves creating a Python virtual environment and installing the necessary Torch dependencies. Once you activate the 'openmmlab' virtual environment, the next step is to install the required PyTorch dependencies. To obtain the necessary checkpoint file. Executing this command will download both the checkpoint and the configuration file directly into your current working directory. For testing our setup, we conducted an inference test using a sample image with the RTMDet model. This step is crucial to verify the effectiveness of the installation and setup.

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Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Comments: Technical report of MMDetection. CV ; Machine Learning cs. LG ; Image and Video Processing eess.

MMRotate is an open-source toolbox for rotated object detection based on PyTorch. It is a part of the OpenMMLab project. MMRotate provides three mainstream angle representations to meet different paper settings. We decompose the rotated object detection framework into different components, which makes it much easy and flexible to build a new model by combining different modules. The toolbox provides strong baselines and state-of-the-art methods in rotated object detection. We are excited to announce our latest work on real-time object recognition tasks, RTMDet , a family of fully convolutional single-stage detectors.

Mmdetection

Released: Jan 5, View statistics for this project via Libraries. Tags computer vision, object detection. MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project. We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. The toolbox directly supports multiple detection tasks such as object detection , instance segmentation , panoptic segmentation , and semi-supervised object detection. All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including Detectron2 , maskrcnn-benchmark and SimpleDet.

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Packages 0 No packages published. Custom properties. Overview of Benchmark and Model Zoo. Overview of Benchmark and Model Zoo. To migrate from MMDetection 2. MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. While installation steps ran smoothly, we encountered a significant hurdle: a failed inference attempt with the MMDetection API. We appreciate all the contributors as well as users who give valuable feedbacks. Influence Flower What are Influence Flowers? Last commit date.

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.

Connected Papers What is Connected Papers? Have an idea for a project that will add value for arXiv's community? The first step in preparing your environment involves creating a Python virtual environment and installing the necessary Torch dependencies. Last commit date. Read more. Details can be found in benchmark. For detailed user guides and advanced guides, please refer to our documentation :. Bibliographic Explorer What is the Explorer? Litmaps Toggle. All dependencies were seamlessly handled in the background. You signed in with another tab or window. We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.