Vehicle-Detection | Vehicle detection based on YOLO and SVM | Machine Learning library

by uranus4ever Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Vehicle-Detection Summary

Vehicle-Detection is a Python library typically used in Manufacturing, Utilities, Automotive, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, OpenCV applications. Vehicle-Detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Vehicle-Detection build file is not available. You can download it from GitHub.

Vehicle detection project used machine learning and computer vision techniques, and combined [advanced lane detection] techniques.

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Vehicle-Detection has a low active ecosystem.

It has 10 star(s) with 4 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 454 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Vehicle-Detection is current.

Quality

Vehicle-Detection has no bugs reported.

Security

Vehicle-Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Vehicle-Detection 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

Vehicle-Detection releases are not available. You will need to build from source code and install.

Vehicle-Detection 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.

Top functions reviewed by kandi - BETA

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

Combine SVM classification features
Extract features from images
Binarize an image
Color histogram
Return a list of all boxes in image
Reduces the heatmap below a given threshold
Add a heatmap to a list of bboxes
Draws the bounding boxes of each car
Draw a feature
Convert from image
Searches for features in an image
Extract features from a single image
Combine feature vectors
Color classification
Compute the HOG feature classification
Plots the heatmap of the selected boxes
Visualize a heatmap image
Load weights from a yolo weight file
Draw a test image

Get all kandi verified functions for this library.

Vehicle-Detection Key Features

No Key Features are available at this moment for Vehicle-Detection.

Vehicle-Detection Examples and Code Snippets

No Code Snippets are available at this moment for Vehicle-Detection.

Community Discussions

Trending Discussions on Vehicle-Detection

slice indices must be integers or None or have an __index__ method in udacity self driving

Tensorflow - loss not decreasing

QUESTION

slice indices must be integers or None or have an __index__ method in udacity self driving

Asked 2017-Nov-26 at 06:10

in udacity self driving https://github.com/udacity/self-driving-car/tree/master/vehicle-detection/u-net the method get_mask_seg(img, bb_boxes_f) gives slice indices must be integers or none or have an index method

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ANSWER

Answered 2017-Nov-26 at 04:17

This code is breaking due to a relatively recent change in NumPy. To fix it, you'll need to ensure that the index array bb_box_i is an integer array. The easiest way to do that is probably to add a line of code that does bb_box_i = bb_box_i.astype('int') before indexing into the img_mask array.

Source https://stackoverflow.com/questions/47492955

QUESTION

Tensorflow - loss not decreasing

Asked 2017-Jul-20 at 15:33

Lately, I have been trying to replicate the results of this post, but using TensorFlow instead of Keras. However, my model loss is not converging as in the code provided. I took care to use the same parameters used by the author, even those not explicitly shown. My complete code can be seen here.

I have already tried different learning rates, optimizers, and batch sizes, but these did not affect the result very much as well.

I found a bunch of other questions related to this problem here in StackOverflow and StackExchange, but most of them had no answer at all. The questions with answers, however, did not help.

I'm using TensorFlow 1.1.0, Python 3.6 and Windows 10.

The most weird thing is that we have the same database and the same model, but just different frameworks. Thus, it was not supposed to give completely different behaviours. Does anyone have suggestions about what should I try to solve this problem, please?

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ANSWER

Answered 2017-Jul-20 at 15:33

I ran your code basically unmodified, but I looked at the shape of your tf_labels and logits and they're not the same. logits had shape (batch_size,1,1,1) (because you were using a 1x1 convolutional filter) and tf_labels had shape (batch_size,1). I changed your loss line to be

Source https://stackoverflow.com/questions/45191767

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

Vulnerabilities

No vulnerabilities reported

Install Vehicle-Detection

You can download it from GitHub.
You can use Vehicle-Detection 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 .

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