kandi X-RAY | pytorch-yolov3 Summary
kandi X-RAY | pytorch-yolov3 Summary
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 nrsyed.com.
Top functions reviewed by kandi - BETA
- 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
pytorch-yolov3 Key Features
pytorch-yolov3 Examples and Code Snippets
Trending Discussions on pytorch-yolov3
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
ANSWERAnswered 2021-Mar-10 at 16:13
You are probably using the wrong python binary. Can you try
python test.py --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):
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:...
ANSWERAnswered 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.
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?...
ANSWERAnswered 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
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...
ANSWERAnswered 2019-Dec-01 at 12:38
Pass extra parameters for tqdm
unit='B', unit_scale=True, unit_divisor=1024.
These are pytorch-yolo v3 code. I downloaded it in github. (https://github.com/eriklindernoren/PyTorch-YOLOv3) I tuned this for two classes. And while I'm doing trainning, there is still an error.
This is test.py code....
ANSWERAnswered 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:
No vulnerabilities reported
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