CenterNet | Object detection , 3D detection | Computer Vision library

 by   xingyizhou Python Version: Current License: MIT

kandi X-RAY | CenterNet Summary

kandi X-RAY | CenterNet Summary

CenterNet is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow applications. CenterNet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Object detection, 3D detection, and pose estimation using center point detection:. Objects as Points, Xingyi Zhou, Dequan Wang, Philipp Krähenbühl, arXiv technical report (arXiv 1904.07850).

            kandi-support Support

              CenterNet has a medium active ecosystem.
              It has 6889 star(s) with 1913 fork(s). There are 112 watchers for this library.
              It had no major release in the last 6 months.
              There are 428 open issues and 559 have been closed. On average issues are closed in 77 days. There are 9 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of CenterNet is current.

            kandi-Quality Quality

              CenterNet has 0 bugs and 0 code smells.

            kandi-Security Security

              CenterNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              CenterNet code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              CenterNet is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              CenterNet releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              CenterNet saves you 3948 person hours of effort in developing the same functionality from scratch.
              It has 8404 lines of code, 479 functions and 80 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CenterNet and discovered the below as its top functions. This is intended to give you an instant insight into CenterNet implemented functionality, and help decide if they suit your requirements.
            • Decode heatmap
            • Left aggregation function
            • Right aggregation function
            • Compute nms of a heatmap
            • Counts the number of anchors for each image
            • Generate thumbnail anchors
            • Convert a coco box to a bbox
            • Evaluate recall
            • Returns the intersection between two bounding boxes
            • Example demo
            • Create a convolution layer
            • Debugging function
            • Debugger for debugging
            • Wrapper for parallel_apply
            • Debugger function
            • Count the number of images in the given split
            • Process the model
            • Locate the CUDA
            • Load a trained model
            • Counts the number of images in the given split
            • Count the number of images in the image
            • Convert a coco box to bbox
            • Update the information of the dataset
            • Parse arguments
            • Wrapper for agnex_decode
            • Runs ddd prediction
            • Forward computation
            Get all kandi verified functions for this library.

            CenterNet Key Features

            No Key Features are available at this moment for CenterNet.

            CenterNet Examples and Code Snippets

            CenterNet with Unsupervised Domain Adaptation methods,Usage,Configuration
            Pythondot img1Lines of Code : 122dot img1License : Permissive (MIT)
            copy iconCopy
            experiment: default # experiment name and also folder name (outputs/default) where logs a.s.o. are saved
            # path to pretrained weights
            # optimizer states are not restored
            pretrained: /mnt/data/Projects/centernet-uda/weights/coco_dla_2x.pth
            # path to  
            CenterNet with Unsupervised Domain Adaptation methods,Dataset
            Pythondot img2Lines of Code : 43dot img2License : Permissive (MIT)
            copy iconCopy
              "images": [
                  "id": 1,
                  "width": 1680,
                  "height": 1680,
                  "file_name": "Record_00600.jpg",
                  "license": 0,
                  "flickr_url": "",
                  "coco_url": "",
                  "date_captured": 0
                  "id": 17,
            Pythondot img3Lines of Code : 37dot img3License : Permissive (MIT)
            copy iconCopy
            _defaults = {
                #   使用自己训练好的模型进行预测一定要修改model_path和classes_path!
                #   model_path指向logs文件夹下的权值文件,classes_path指向model_data下的txt
                #   如果出现shape不匹配,同时要注意训练时的model_pat  
            gluon-cv - demo center net
            Pythondot img4Lines of Code : 12dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            """11. Predict with pre-trained CenterNet models
            This article shows how to play with pre-trained CenterNet models with only a few
            lines of code.
            First let's import some necessary libraries:

            Community Discussions


            Tensorflow Object Detection API GPU memory issue
            Asked 2021-Mar-11 at 07:39

            I'm currently trying to train a model based off the model detection zoo for object detection. Running the setup on the CPU works as expected but trying the same on my GPU results in the following error.



            Answered 2021-Mar-11 at 07:39

            I've done a complete reinstallation of every involving component. I might have done something different this time but I cannot say what. Atleast I'm now able to utilize the GPU for training.



            Accessing to Weights and Layers in Tensorflow Hub
            Asked 2021-Feb-17 at 07:44

            When I try to get the model from tensorflow-hub resporitory. I can see it as a Saved Model format, but I cant get access to model architecture as well as weights store for each layer.



            Answered 2021-Feb-16 at 08:48

            With the CLI tool saved_model_cli provided by the package tensorflow-serving-api it's possible to inspect a saved model. In the first step I downloaded and cached the model:



            Gtk-WARNING **: 13:51:34.650: cannot open display
            Asked 2020-Aug-25 at 03:17

            I am running a docker container for a CV Deep Learning project. Before running the docker container:



            Answered 2020-Aug-25 at 03:17

            Before running the container, the following steps are required:



            Loading ResNet50 on RTX2070 - Out of Memory
            Asked 2020-Jan-14 at 13:05

            I'm trying to load ResNext50, and on top of it CenterNet, I'm able to do it with Google Colab or Kaggle's GPU. But,

            1. Would love to know how much GPU Memory (VRAM) does this network need?

            2. When using RTX 2070 with free 5.5GB VRAM left on it (out of 8GB), I'm not able to load it.

            Batch size is 1, #of workers is 1, everything is set to minimum values. OS: Ubuntu 18.04 (Using PyTorch)

            In TensorFlow, I know that I can restrict the amount of VRAM (which enables me to load and run networks although I don't have enough VRAM), but in PyTorch I didn't find this functionality yet.

            Any ideas how to solve this?



            Answered 2020-Jan-14 at 13:05
            Using third party dependency

            You could get size of model in bytes using third party library torchfunc (disclaimer I'm the author).


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install CenterNet

            Please refer to for installation instructions.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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