pytorch-yolov3 | pytorch implementation of YOLOv3 for real-time webcam | Computer Vision library

 by   nrsyed Python Version: Current License: MIT

kandi X-RAY | pytorch-yolov3 Summary

kandi X-RAY | pytorch-yolov3 Summary

pytorch-yolov3 is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. pytorch-yolov3 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This package is a from-scratch implementation of YOLOv3 in PyTorch capable of running in real time on webcam streams as well as on image files and video files. It parses the original Darknet configuration and weights files to build the network and has been tested with the yolov3, yolov3-tiny, and yolov3-spp models. A more detailed treatment of YOLOv3 and the code in this repo can be found in the blog posts at

            kandi-support Support

              pytorch-yolov3 has a low active ecosystem.
              It has 10 star(s) with 3 fork(s). There are 1 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-yolov3 is current.

            kandi-Quality Quality

              pytorch-yolov3 has no bugs reported.

            kandi-Security Security

              pytorch-yolov3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              pytorch-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

              pytorch-yolov3 releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pytorch-yolov3 and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-yolov3 implemented functionality, and help decide if they suit your requirements.
            • Detect boxes in a video
            • Wrapper for inference
            • Draw boxes
            • R Computes the suppression of a non - insufficient suppression
            • Non - maximum suppression
            • Convert from cxywh totl coordinates
            • Generate unique RGB colors
            • Read from the cap
            • Stop the thread
            • Load weights from file
            • Draw the images in the given image
            • Detect images in a video
            • Write video frames to filepath
            Get all kandi verified functions for this library.

            pytorch-yolov3 Key Features

            No Key Features are available at this moment for pytorch-yolov3.

            pytorch-yolov3 Examples and Code Snippets

            No Code Snippets are available at this moment for pytorch-yolov3.

            Community Discussions


            Conda - ModuleNotFoundError: No module named 'torch'
            Asked 2021-Mar-10 at 16:13

            Steps to reproduce:

            I am using Anaconda on Windows to set up environment for this repo.

            conda create --name pytorch-yolo

            Then I install all dependencies with conda install --file requirements.txt

            Which returns



            Answered 2021-Mar-10 at 16:13

            You are probably using the wrong python binary. Can you try python --weights_path weights/yolov3.weights?

            I am not familiar with Windows terminal, but you can get the path to the binaries by using the where command (which for Linux):



            Pytorch executeable works while running from Anaconda prompt but not from Cmd or .exe?
            Asked 2020-Nov-19 at 14:20

            I packaged (using Pyinstaller) a small variant of the Minimalistic Yolo github repo, found Here, the packaging was done using pyinstaller to run the object detection as a server using Flask.

            So while attempting to run the server, it only works when running from Anaconda Prompt (Which is where i wrote the pyinstaller command) other than that, the following error occur.

            Error i Get while running from (exe,Cmd,PowerShell) is:



            Answered 2020-Nov-19 at 13:29

            Alright, turns out this is an issue with pyinstaller.

            if Pytorch is installed using Conda, it requires the CUDANN , and it won't work with it (ie without that environment)

            if you want it to work every where, Pytorch has to be installed using pip.

            For reference,



            PyTorch-YOLOv3 Accuracy Metrics
            Asked 2019-Dec-11 at 12:13

            I'm using an existing PyTorch-YOLOv3 architecture and training it to recognize a custom dataset through google colab for a research manuscript. Basically I want to use the object detection algorithm to count the number of objects for two classes in an image.

            I've been told that for my purpose, I should generate validation/training curves for the model and create a confusion matrix to evaluate the classifier element of the trained model. I have an idea to modify the training script to output training metrics to a csv file during the training, but I'm not familiar with how to create a confusion matrix to evaluate the trained model.

            Additionally, in the field of computer vision, what kind of metrics/figures should be generated for a manuscript?



            Answered 2019-Dec-11 at 12:13

            Regarding the first part of your question, since you seem to only be concerned with two classes, a simple confusion matrix would look like



            tqdm to display download in human readable units
            Asked 2019-Dec-01 at 12:38

            I am using tqdm and requests to manage file download in Python. However I can't figure out how to make tqdm display the progress bar in human-readable format i.e. in MB/s.

            Here is my code



            Answered 2019-Dec-01 at 12:38

            Pass extra parameters for tqdm unit='B', unit_scale=True, unit_divisor=1024.




            How to solve ValueError: not enough values to unpack error
            Asked 2019-Jun-25 at 14:38

            These are pytorch-yolo v3 code. I downloaded it in github. ( I tuned this for two classes. And while I'm doing trainning, there is still an error.

            This is code.



            Answered 2019-May-13 at 18:44

            It seems that this list of comprehension: [np.concatenate(x, 0) for x in list(zip(*sample_metrics))] is empty. It is hard to say since I don't know how sample_metrics looks like, because I don't see definition of get_batch_statistics in this sentence: sample_metrics += get_batch_statistics(outputs, targets, iou_threshold=iou_thres).

            But this might helps. A statement like this:


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


            No vulnerabilities reported

            Install pytorch-yolov3

            Clone the repo, install the package, and download weight files to the models directory:.


            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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