You Only Look Once

To date, algorithms have been proposed that achieve good performance according to these criteria – R-CNN and Fast R-CNN.Using Data Mining algorithms and subsequent filtering, we obtained a dataset with 1500 low-resolution images and a test set of images.In this algorithm, a minimum loss of accuracy was achieved with the help of high performance both in image recognition and in video streaming.Object detection is a computer vision task.The main task was to obtain detection accuracy above 60% in a small dataset.It allows fast image processing (about 1000 times faster than R-CNN and 100 times faster than Fast R-CNN).We continue working on application development taking into account the modifications which will help us to improve the dataset for more accuracy.

For a computer, such tasks are more complicated.[XRP Review Wallets 2019 Best]


The quality of the datasets directly affects the accuracy of the model.


The accuracy of predictions was about 60-75% on groups of bees.In the first part of our article, we talked about objects classification, ways of model training, and the whole architecture of the application.For example, when crossing an intersection, drivers are guided by a set of knowledge and conditions – they recognize the color of the traffic light or road signs.The YOLO algorithm was developed specifically for object detection and object recognition.During the model testing, we realized that our dataset can be improved for more accuracy.The process of achieving this success will be described below.[ETH Ever Top Block Quits Daily Rewards Lowest Dev]

Experiment 1

Experiment 2 


Soon we will tell you about the last stage of product development and app release, so stay tuned for that!

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SoftTeco continues to explore machine learning technology and the possibility of object detection in beekeeping.First of all, the algorithm for detecting objects in images or video should have high accuracy and speed in the object location definition, as well as its classification.Our dataset needed processing to solve the object detection problem.

Not so long ago, we got successful results and solved another machine learning task – object detection.The image markup was made in a cross-platform GUI tool to annotate images.In daily life, people usually solve the same problems.Object detection is the processing of images or video files under certain conditions.On a dataset with 7000 labeled images, the accuracy of prediction will be 85-98%.For the learning process, we used the most powerful GPU that exists at the moment.[Current Price LTC Litecoin]

Object detection

Before we get into the details of the model’s creation, let’s first define what object detection is.This term is mentioned in the article “Eyes and Ears of a Computer” by Oliver Selfridge.Testing of the model showed excellent results.We also chose this image processing method because of its simple integration with PyTorch.
Source: https://www.softteco.com/blog/machine-learning-in-beekeeping-part-2/

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