YOLOv3 | Keras implementation of yolo v3 object detection | Computer Vision library

 by   xiaochus Python Version: v1.0 License: MIT

kandi X-RAY | YOLOv3 Summary

kandi X-RAY | YOLOv3 Summary

YOLOv3 is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, Keras applications. YOLOv3 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However YOLOv3 build file is not available. You can download it from GitHub.

Keras(TF backend) implementation of yolo v3 objects detection. According to the paper YOLOv3: An Incremental Improvement.
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            kandi-support Support

              YOLOv3 has a low active ecosystem.
              It has 599 star(s) with 263 fork(s). There are 23 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 15 open issues and 9 have been closed. On average issues are closed in 153 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of YOLOv3 is v1.0

            kandi-Quality Quality

              YOLOv3 has 0 bugs and 0 code smells.

            kandi-Security Security

              YOLOv3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              YOLOv3 code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              YOLOv3 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

              YOLOv3 releases are available to install and integrate.
              YOLOv3 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.
              YOLOv3 saves you 179 person hours of effort in developing the same functionality from scratch.
              It has 443 lines of code, 19 functions and 4 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed YOLOv3 and discovered the below as its top functions. This is intended to give you an instant insight into YOLOv3 implemented functionality, and help decide if they suit your requirements.
            • Detects the video using the given video
            • Run yolo output
            • Process the features
            • Calculate the nms of boxes
            • Draws boxes
            • Predict an image
            • Filter boxes according to threshold
            • Predict boxes
            • Process an image
            • Sigmoid function
            • Runs the image
            • Generate a stream of unique section names
            • Return a list of class names
            • Darknet layer
            • Conv2d convolutional layer
            • Base layer
            • Resolve a residual block
            • Stack a residual block
            Get all kandi verified functions for this library.

            YOLOv3 Key Features

            No Key Features are available at this moment for YOLOv3.

            YOLOv3 Examples and Code Snippets

            YOLOv3: An Incremental Improvement
            pypidot img1Lines of Code : 6dot img1no licencesLicense : No License
            copy iconCopy
            @article{yolov3,
              title={YOLOv3: An Incremental Improvement},
              author={Redmon, Joseph and Farhadi, Ali},
              journal = {arXiv},
              year={2018}
            }
            
              

            Community Discussions

            QUESTION

            How to upload an Image File modified by OpenCV using FileSystemStorage in Django?
            Asked 2022-Feb-13 at 16:57

            I am taking an uploaded image from the user and then sending it to a YOLO model which then returns me an image.

            I want to store that returned image in my Local Directory and then display it on the user interface.

            This is the Code of views.py that takes in an image and sends it to the Yolo Model,

            ...

            ANSWER

            Answered 2022-Feb-13 at 16:57

            You can use the imwrite function of cv2 library to store your files in the local directory, i.e.,

            In your case, simply do this,

            Source https://stackoverflow.com/questions/71100904

            QUESTION

            OpenVINO MULTI:MYRIAD with sequential inference is inefficiency and usually shows "XLink_sem_wait:94" and "XLinkResetRemote:257" logs
            Asked 2022-Jan-13 at 02:25

            I have integrated OpenVINO and PyQt5 to do the inference job as shown in the image on Windows 11 with openvino_2021.4.689 version.

            I reference this GitHub to finish YOLOv4 inference with NCS2.

            The following is my inference engine code.

            ...

            ANSWER

            Answered 2022-Jan-13 at 02:25

            The optimum way to use this Multi-plugin with multiple devices is by configuring the individual devices and creating the Multi-Device on top.

            For example:

            Source https://stackoverflow.com/questions/70639620

            QUESTION

            How to show few correct bounding boxes instead of all detectiones in YOLOv3
            Asked 2022-Jan-05 at 18:39

            This is my first YOLOv3 project to detect objects with 2 classes. In the resuls I am seeing plenty of detections. Is it possible to show only those boxes which has more than 70% (for example) accuracy or lets say top 3 boxes for each class? Would somebody please help?

            my test is executed as follows: !./darknet detector test data/obj.data cfg/yolov3_custom.cfg ~/yolov3_custom_last.weights /content/5.png

            ...

            ANSWER

            Answered 2022-Jan-05 at 10:35

            You must set 0.7 threshold value, if you want see only boxes which has more than 70% confidence score.

            like this !./darknet detector test data/obj.data cfg/yolov3_custom.cfg ~/yolov3_custom_last.weights /content/5.png -thresh 0.7

            Source https://stackoverflow.com/questions/70580221

            QUESTION

            tensor.shape return a list of None values when using tf.keras
            Asked 2021-Nov-20 at 10:12

            I have a function which accepts a tensor and calculates the num_classes variable from the shape of this tensor using an equation which looks something like this:

            ...

            ANSWER

            Answered 2021-Oct-21 at 13:40

            Maybe try the following code:

            Source https://stackoverflow.com/questions/69662822

            QUESTION

            Loading YOLO: invalid index to scalar variable
            Asked 2021-Nov-08 at 09:29

            Getting an error for IndexError: invalid index to scalar variable on the yolo_layers line.

            ...

            ANSWER

            Answered 2021-Nov-08 at 09:29

            It's may caused by the different versions of cv2. The version of cv2 module with CUDA support will give you a 2-D array when calling network.getUnconnectedOutLayers(). However, the version without CUDA support will give a 1-D array.

            You may try to take the brackets out which closing the index 0.

            Source https://stackoverflow.com/questions/69834335

            QUESTION

            Tensorflow conflict in ImageAI pipeline
            Asked 2021-Oct-28 at 11:39

            As a Python 3.9.7 user, I created an environment which has:tensorflow-gpu==2.6.0, keras==2.6.0 and also using imageai, however, whenever I run the code, I get the error:

            ...

            ANSWER

            Answered 2021-Oct-28 at 11:39

            Ok, so what managed to solve the dependency conflict was - after installing all requirements, I updated imageai with:

            Source https://stackoverflow.com/questions/69740243

            QUESTION

            Darknet Yolov3 - Custom training on pre-trained model
            Asked 2021-Oct-25 at 02:03

            Actually in darknet yolov3 model has coco.names file for labels which include 80 classes. Now if I want to train a custom model with two labels only, where one label is already there in coco.names and another is not there.

            For example I want to train a model to detect for cell phone and dslr camera, so cell phone class already exist in coco.names whereas dslr camera is not there in its labels file.

            So can I train custom model using two classes cell phone and dslr camera and give data of only dslr camera for training and it will predict for both dslr camera and cell phone or shall I train with both data of cell phone and dslr images or is there any other way out.

            I am a bit new to ML, so any help would be great Thanks

            ...

            ANSWER

            Answered 2021-Oct-25 at 02:03

            So you want to fine tune a pre-trained model. You need to think of classes by just being a set of end nodes of a network, the labels (phone, camera) are just a naming convention for them, and to give us visual guidance.

            These nodes are fully connected (with associated weights) to the previous layer of the network, the total number of these intermediate connections varies depending on the number of end nodes (classes) you have.

            With the fully trained model, you can't just select the nodes you want, and take out the rest, and add a few more. Because the previous layer (and full network) was trained to give estimates/predictions taking into account a certain number of final nodes.

            So basically you need to give a full reset on the last layer (the head), and restart it with the desired number of classes. The idea here, is that you take advantage of the previous training effort on a broader dataset, and fine tune it to your desired data.

            Short answer, you need data for both, and need to change the model to accept 2 classes only.

            To configure that specific model for the new number of classes and data, I believe you can find some guidance and instructions here

            Source https://stackoverflow.com/questions/69699422

            QUESTION

            YoloV3 Result Giving Zero confidence in every class
            Asked 2021-Sep-24 at 02:39

            I Am Doing Implementation Of Yolo v3 for multi class object detection

            yolo is algorithm based region proposal and the region proposal with max confidence treated as prediction of yolo for more you can read it about here

            for this particular task i refer to this murtuza tutorial that guided me from scratch

            now as the complex network architecture requires hours of training i prefer to use transfer learning as using pretrained network and weighs(parameters) both of this link you can find here
            Architecture configuration:cfg
            Network Parameters(weights):weights

            i used here yolov3 tiny as i required higher frame rates to process a video but afterall it not giving promising result as the tutorial shows i don't where i am lacking but even changing the network cfg and weight file to the orignal yolov3(320)does not giving true result as i am getting all 5 spatial data as coordinate and confidence [cx,cy,h,w,confidence] but all 80 classes probality is still zero vector[0.0,0.0,0.0---0.0] even changing the video source and choosing another video resulting into zero vector which was in tutorial working fine

            Implementation Code:

            ...

            ANSWER

            Answered 2021-Sep-24 at 02:39

            You have many problems with your code.

            1. You have to use the h,w that you got from the image and not your default width and height that you use to blob the image for the YoloV3.

            change

            Source https://stackoverflow.com/questions/69244618

            QUESTION

            Demo needs OpenCV for webcam images.(opencv is installed and set opencv4=1)
            Asked 2021-Sep-13 at 07:13

            I am trying to do object detection from a video file by using https://github.com/pjreddie/darknet. I've installed libopencv-dev for opencv. I've set opencv4=1 in Makefile.

            And run this code. ./darknet detector demo cfg/coco.data cfg/yolo-tiny-obj.cfg yolov3.weights data/1.mp4 And got error Demo needs OpenCV for webcam images.

            Could anyone help me? Thanks.

            ...

            ANSWER

            Answered 2021-Sep-13 at 07:13
            1. Try to clean make file and recompile darknet

            Source https://stackoverflow.com/questions/69151313

            QUESTION

            How do i stream a video with openCV into my pytorch neural network?
            Asked 2021-Aug-19 at 18:33

            I wrote YOLOv3 in Pytorch from scratch. If i send an image through the model with trained weights, it kinda works. Next step is to use my camera to make YOLO do its magic in real time.

            I think the correct working pipeline is to catch a single frame of the video and feed it to the network. Than, write the boxes on the very same frame.

            ...

            ANSWER

            Answered 2021-Aug-19 at 12:15

            Based on the error message, model is not a class instance. Note, in the Traceback, that

            Source https://stackoverflow.com/questions/68846681

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

            Vulnerabilities

            No vulnerabilities reported

            Install YOLOv3

            Download official yolov3.weights and put it on top floder of project. Run the follow command to convert darknet weight file to keras h5 file. The yad2k.py was modified from allanzelener/YAD2K.
            Download official yolov3.weights and put it on top floder of project.
            Run the follow command to convert darknet weight file to keras h5 file. The yad2k.py was modified from allanzelener/YAD2K.
            run follow command to show the demo. The result can be found in images\res\ floder.

            Support

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