CenterNet | Keypoint Triplets for Object Detection | Computer Vision library

by Duankaiwen Python Version: Current License: MIT

X-Ray Key Features Code Snippets(4)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | CenterNet Summary

CenterNet is a Python library typically used in Artificial Intelligence, Computer Vision applications. CenterNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However CenterNet build file is not available. You can download it from GitHub.

CenterNet is a framework for object detection with deep convolutional neural networks. You can use the code to train and evaluate a network for object detection on the MS-COCO dataset.

Support

Quality

Security

License

Reuse

Support

CenterNet has a medium active ecosystem.

It has 1821 star(s) with 392 fork(s). There are 45 watchers for this library.

It had no major release in the last 6 months.

There are 77 open issues and 69 have been closed. On average issues are closed in 23 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CenterNet is current.

Quality

CenterNet has 0 bugs and 0 code smells.

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.

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.

Reuse

CenterNet releases are not available. You will need to build from source code and install.

CenterNet has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

CenterNet saves you 1413 person hours of effort in developing the same functionality from scratch.

It has 3160 lines of code, 234 functions and 36 files.

It has medium 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.

KP detection
Return the path to the image file
Return the detections of an image
Shuffle the indices
Train the model
Load pretrained model
Saves the current state of the model
Load model parameters from file
Helper function for parallel_apply
Scatter function
Split inputs into kwargs
Evaluate COCO
Create an index for the dataset
Loads the COCO object from a file
Show annotated images
Decodes an array of blob objects
Download all images to tarDir
Load one or more images
Forward computation
Load the COCO data
Parse command line arguments
Fetch data from queue
Load detections
Perform a forward computation
Convert an annotation to a mask
Calculate the loss function

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

Python

Lines of Code : 122

License : Permissive (MIT)

Copy

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

Python

Lines of Code : 43

License : Permissive (MIT)

Copy

  ...
  "images": [
    {
      "id": 1,
      "width": 1680,
      "height": 1680,
      "file_name": "Record_00600.jpg",
      "license": 0,
      "flickr_url": "",
      "coco_url": "",
      "date_captured": 0
    },
    ...

  {
      "id": 17,

预测步骤,b、使用自己训练的权重

Python

Lines of Code : 37

License : Permissive (MIT)

Copy

_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

Python

Lines of Code : 12

License : Non-SPDX (Apache License 2.0)

Copy

"""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:
"""

from

Community Discussions

Trending Discussions on CenterNet

Tensorflow Object Detection API GPU memory issue

Accessing to Weights and Layers in Tensorflow Hub

Gtk-WARNING **: 13:51:34.650: cannot open display

Loading ResNet50 on RTX2070 - Out of Memory

QUESTION

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.

...

ANSWER

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.

Source https://stackoverflow.com/questions/66563512

QUESTION

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.

...

ANSWER

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:

Source https://stackoverflow.com/questions/66115204

QUESTION

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:

...

ANSWER

Answered 2020-Aug-25 at 03:17

Before running the container, the following steps are required:

Source https://stackoverflow.com/questions/63364094

QUESTION

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,

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

...

ANSWER

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).

Source https://stackoverflow.com/questions/59728088

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

Vulnerabilities

No vulnerabilities reported

Install CenterNet

You can download it from GitHub.
You can use CenterNet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

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 .

Find more information at: