DSOD | Learning Deeply Supervised Object Detectors from Scratch | Machine Learning library

by szq0214 Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | DSOD Summary

DSOD is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. DSOD has no bugs, it has no vulnerabilities and it has low support. However DSOD build file is not available and it has a Non-SPDX License. You can download it from GitHub.

DSOD focuses on the problem of training object detector from scratch (without pretrained models on ImageNet). To the best of our knowledge, this is the first work that trains neural object detectors from scratch with state-of-the-art performance. In this work, we contribute a set of design principles for this purpose. One of the key findings is the deeply supervised structure enabled by dense layer-wise connections, plays a critical role in learning a good detection model. Please see our paper for more details.

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Quality

Security

License

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Support

DSOD has a low active ecosystem.

It has 699 star(s) with 211 fork(s). There are 48 watchers for this library.

It had no major release in the last 6 months.

There are 35 open issues and 8 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DSOD is current.

Quality

DSOD has 0 bugs and 91 code smells.

Security

DSOD has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DSOD code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DSOD has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

DSOD 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.

DSOD saves you 1638 person hours of effort in developing the same functionality from scratch.

It has 3636 lines of code, 43 functions and 8 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed DSOD and discovered the below as its top functions. This is intended to give you an instant insight into DSOD implemented functionality, and help decide if they suit your requirements.

Inception V3 body
Layer convolution layer
Return a list of num elements
Inception tower
Gradient of DSodel V6
Create a multi box head
Definition of DSOD300 V3
ResNet 3 layer
ResBody layer
ResNet 2
Gated module
Creates an annotation layer
Get the label name of the given label
Make directory if it does not exist
Check if path exists

Get all kandi verified functions for this library.

DSOD Key Features

No Key Features are available at this moment for DSOD.

DSOD Examples and Code Snippets

No Code Snippets are available at this moment for DSOD.

Community Discussions

Trending Discussions on DSOD

How to solve Java resttemplate post having map instead of valid json bad request Error 400

get Values from Json List java

Tensorflow + Keras training: InvalidArgumentError: Incompatible shapes: [7,128,2,2] vs [7,128,3,3]

QUESTION

How to solve Java resttemplate post having map instead of valid json bad request Error 400

Asked 2020-Aug-07 at 09:13

I am programming a Spring Boot Application, that should send a JSON via POST-Request to my REST-API.

My Controller class looks like:

...

ANSWER

Answered 2020-Aug-07 at 08:58

Have out tried with: MultiValueMap parameters = new LinkedMultiValueMap<>(); ?

Source https://stackoverflow.com/questions/63297382

QUESTION

get Values from Json List java

Asked 2020-Aug-05 at 13:18

I have a SpringBootApplication with REST-MVC like following Code examples: I have a Service that looks like this:

...

ANSWER

Answered 2020-Aug-05 at 10:32

Since you are using Spring Boot you should consider to use RestTemplate.

Source https://stackoverflow.com/questions/63262355

QUESTION

Tensorflow + Keras training: InvalidArgumentError: Incompatible shapes: [7,128,2,2] vs [7,128,3,3]

Asked 2019-Jun-06 at 10:40

Implementing and Training Tiny-DSOD network on tensorflow + keras. When starting 1st epoch, training is terminated with error: tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [7,128,2,2] vs. [7,128,3,3]

Batch size is 8, image size is (300,300) and the dataset used to train is PASCAL VOC 2007+2012. The error occurs between one of the outputs to the prediction layer(very similar to SSD) and loss: [[{{node add_fpn_0_/add}}]] [[{{node loss/add_50}}]]

Currently, the version of tensorflow is 1.13 and keras is 2.2.4. Python version is 3.6. I have checked everything from the model itself(the shapes are as expected), images being generated for the batches(each image is as expected), changing loss computation(currently using Adam, but tried with SGD as well, it is exactly the same problem.) and checked tensorboard if can provide any information(everything goes well until that point of termination).

...

ANSWER

Answered 2019-Jun-04 at 17:16

i think that the problem is the images dimensions inside the network.

try change this part:

Source https://stackoverflow.com/questions/56445615

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

Vulnerabilities

No vulnerabilities reported

Install DSOD

You can download it from GitHub.
You can use DSOD 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.