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Yolov3 darknet weights in python

yolov3 darknet weights in python

!wget dush4kam.ru функция для загрузки весов из оригинальной тренированной модели YOLO в Darknet. Я обрабатываю видео в darkflow с помощью dush4kam.ru и dush4kam.rus, и я получаю очень Я пытаюсь использовать модель darknet yolov3 в моем коде Python. /usr/bin/env python. """ Reads Darknet config and weights and creates Keras model with TF backend. """ import argparse. import configparser. import io.

Yolov3 darknet weights in python

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Every predicted box is associated with a confidence score. In the first stage, all the boxes below the confidence threshold parameter are ignored for further processing. The rest of the boxes undergo non-maximum suppression which removes redundant overlapping bounding boxes.

Non-maximum suppression is controlled by a parameter nmsThreshold. You can try to change these values and see how the number of output predicted boxes changes. You can also change both of them to to get faster results or to to get more accurate results.

The file coco. We read class names. You could try setting the preferable target to cv. In this step we read the image, video stream or the webcam. In addition, we also open the video writer to save the frames with detected output bounding boxes. The input image to a neural network needs to be in a certain format called a blob. After a frame is read from the input image or video stream, it is passed through the blobFromImage function to convert it to an input blob for the neural network.

It also resizes the image to the given size of , without cropping. Note that we do not perform any mean subtraction here, hence pass [0,0,0] to the mean parameter of the function and keep the swapRB parameter to its default value of 1. These boxes go through a post-processing step in order to filter out the ones with low confidence scores. We will go through the post-processing step in more detail in the next section. We print out the inference time for each frame at the top left. The image with the final bounding boxes is then saved to the disk, either as an image for an image input or using a video writer for the input video stream.

Since we want to run through the whole network, we need to identify the last layer of the network. We do that by using the function getUnconnectedOutLayers that gives the names of the unconnected output layers, which are essentially the last layers of the network. Then we run the forward pass of the network to get output from the output layers, as in the previous code snippet net.

The fifth element represents the confidence that the bounding box encloses an object. The rest of the elements are the confidence associated with each class i. The box is assigned to the class corresponding to the highest score for the box. The highest score for a box is also called its confidence. If the confidence of a box is less than the given threshold, the bounding box is dropped and not considered for further processing. The boxes with their confidence equal to or greater than the confidence threshold are then subjected to Non Maximum Suppression.

This would reduce the number of overlapping boxes. The Non Maximum Suppression is controlled by the nmsThreshold parameter. If nmsThreshold is set too low, e. But if it is set too high e. So we used an intermediate value of 0. The gif below shows the effect of varying the NMS threshold. Finally, we draw the boxes that were filtered through the non maximum suppression, on the input frame with their assigned class label and confidence scores.

We used video clips from the following sources: Pixabay: [1] , [2] , [3] , [4] , [5] , [6] Pexels: [2]. Skip to primary navigation Skip to main content Skip to primary sidebar Skip to footer. Learn More. Download Code To easily follow along this tutorial, please download code by clicking on the button below.

Download Code. VideoCapture args. Check the args. You should set the parameters yourself in your own specific task. Using eval. The parameters are as following:. Check the eval. You should set the parameters yourself.

Second stage: Restore the weights from the first stage, then train the whole model with small learning rate like 1e-4 or smaller. At this stage remember to restore the optimizer parameters if you use optimizers like adam. In this condition, be careful about the possible nan loss value. These are all good strategies but it does not mean they will definitely improve the performance.

You should choose the appropriate strategies for your own task. This paper from gluon-cv has proved that data augmentation is critical to YOLO v3, which is completely in consistent with my own experiments. Some data augmentation strategies that seems reasonable may lead to poor performance. For example, after introducing random color jittering, the mAP on my own dataset drops heavily. Thus I hope you pay extra attention to the data augmentation. If you fine-tune the whole model, using adam may cause nan value sometimes.

You can try choosing momentum optimizer. I did a quick train on the VOC dataset. No hard-try fine-tuning. You should get the similar or better results. My pretrained weights on VOC dataset can be downloaded here. Skip to content. Star 1. MIT License. Branches Tags. Could not load branches.

Could not load tags. Latest commit. Git stats 33 commits. Failed to load latest commit information. Jan 19, Jun 28, Jul 1, Dec 20, May 5, Jun 30, Jan 25, Jun 29, Jul 2, View code. Introduction 2. Requirements 3.

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