ssd.pytorch | A PyTorch Implementation of Single Shot MultiBox Detector | Computer Vision library

by amdegroot Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ssd.pytorch Summary

ssd.pytorch is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow applications. ssd.pytorch has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However ssd.pytorch build file is not available. You can download it from GitHub.

A PyTorch Implementation of Single Shot MultiBox Detector

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Support

ssd.pytorch has a medium active ecosystem.

It has 4883 star(s) with 1736 fork(s). There are 85 watchers for this library.

It had no major release in the last 6 months.

There are 354 open issues and 180 have been closed. On average issues are closed in 28 days. There are 19 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ssd.pytorch is current.

Quality

ssd.pytorch has 0 bugs and 0 code smells.

Security

ssd.pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ssd.pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ssd.pytorch 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

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

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

Installation instructions, examples and code snippets are available.

ssd.pytorch saves you 686 person hours of effort in developing the same functionality from scratch.

It has 1587 lines of code, 110 functions and 20 files.

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

Top functions reviewed by kandi - BETA

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

Train the network
Add extra layers to VGG
Build an SSD object
Load weights from file
Test for detections
Set the time of the simulation
Calculate the total time elapsed time
Run python evaluation
Forward computation
Computes the log of the sum of x
Calculate the jaccard similarity
Encodes the matched parameters
Compute nms
Compute the nms of the given boxes
Decode a binary box
Pulls the image at the specified index
Return image at index
Wrapper for cv2
Build an SSD object

Get all kandi verified functions for this library.

ssd.pytorch Key Features

No Key Features are available at this moment for ssd.pytorch.

ssd.pytorch Examples and Code Snippets

SSD.Pytorch,Train with Customer Dataset

Python

Lines of Code : 11

License : Permissive (MIT)

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VOC_CLASSES = (  # always index 0
    ur dataset class)

VOC_ROOT = osp.join('./', "data/VOCdevkit/")

VOC_ROOT = osp.join('./', "data/CityDet/")

image_sets=[('2007', 'trainval'), ('2012', 'trainval')]

image_sets=[('2007', 'trainval')]

mkdir weigh

SSD: Single Shot MultiBox Object Detector, in PyTorch,Training SSD

Python

Lines of Code : 11

License : Permissive (MIT)

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cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
#adjust the keys in the weights file to fit for current model
python3 vggweights.py
cd ..

#use vgg 
python3 train.py 
#If use resNet 
python3 train.py --model 'resnet' --b

Object-Oriented Super-Resolution Imaging,Download Pre-trained Models,Module-2 Objects Super-Resolution with SRGAN

Python

Lines of Code : 8

License : No License

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cd /srgan

#Original SRGAN
python src/train.py --dataset OOIS2012 --upscale_factor 4 --crop_size 44

#improved SRGAN (ref:WGAN)
python src/wgan_train.py --dataset OOIS2012 --upscale_factor --crop_size 44

Community Discussions

Trending Discussions on ssd.pytorch

SSD(single shot detector)'s default box implementation

QUESTION

SSD(single shot detector)'s default box implementation

Asked 2020-May-03 at 07:06

I can't understand SSD's default box implementation. Original paper's formula is below;

w_k=s_k√a_k, h_k=s_k/√a_k

But many SSD's implementation seems to be different above's formula. For example, ssd.pytorch;

...

ANSWER

Answered 2020-May-03 at 07:06

I found the answer in github's issue

UPDATE:

min_sizes/img_size and max_sizes/img_size mean s_k and s_k+1 respectively. Also, conv4_3 applies s_k=0.1 instead of equation(4). Therefore, all of feature maps can't apply equation(4). So I think all of scales are defined as min_sizes and max_sizes beforehand.

Source https://stackoverflow.com/questions/61545482

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

Vulnerabilities

No vulnerabilities reported

Install ssd.pytorch

Install PyTorch by selecting your environment on the website and running the appropriate command.
Clone this repository. Note: We currently only support Python 3+.
Then download the dataset by following the instructions below.
We now support Visdom for real-time loss visualization during training! To use Visdom in the browser: # First install Python server and client pip install visdom # Start the server (probably in a screen or tmux) python -m visdom.server Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).
Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.

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: