Abhishek-Thakur
The video covers the process of training and using YOLOv8, a state-of-the-art object detection model, on a custom dataset using Python. The presenter explains the process of converting the data set into the format expected by YOLOv8 and demonstrates how to create the necessary directories and files for training. They also show how to create a yaml file with the necessary parameters for training the model using pre-trained weights, adjust the parameters, and run the training process. The presenter also discusses how to check for labeling errors, plot the detected objects' bounding boxes, and save the images in PNG format. Finally, the presenter encourages viewers to explore the predict function's documentation and hints at future videos covering segmentation and post tasks.
In this section of the video, the presenter explains how to train YOLO V8 on a custom data set using Python. They highlight the YOLO V8 model, which is claimed to be one of the state-of-the-art object detection models, and discuss the different data formats for object detection that may be used. They also demonstrate how to convert data sets into the format that YOLO expects, which involves using the normalized width and height and X and Y centers. They use the Plastic in Rivers data set as an example of a real-world use case for image object detection and show how to identify the four different classes in the data set.
In this section, the speaker explains how to convert data from a bounding box format to YOLO format for object detection using YOLOv8 in Python. They describe how to normalize the bounding box's x center, y center, width, and height by using simple mathematics, and then divide them by the width and height of the image. The process makes it easier to train YOLO models for object detection. The speaker also demonstrates how to download a YOLOv8 dataset and convert it to this format using the Ultralytics and data sets libraries in Python.
In this section, the presenter explains how to format the dataset for YOLO detection. The dataset should be formatted with a folder for images and labels, and the labels should be in a text file for each image, specifying the label ID, x, y, w, and h in normalized format. Once the dataset is formatted, the presenter shows how to create directories for train and validation images and labels using OS make directories. Finally, a function is created to read the dataset and split it between training and evaluation data.
In this section, the speaker discusses the process of creating the data set for training YOLOv8 in Python. The data set includes the example image as well as the labels which come in another key called litter. The labels and bonding boxes are stored in a list called targets. The speaker then uses the open function to write the target string to a file and saves the image in PNG format. Once the data set has been properly formatted, training YOLOv8 becomes relatively easy.
In this section of the video, the presenter discusses the creation of a yaml file required for training a YOLOv8 model. This file contains information about the location of the data set, the class names, and the image size, among other parameters. The presenter then walks through the process of importing the necessary packages and loading the pre-trained YOLOv8 model from a yaml file. They specify the use of the medium-sized model and make adjustments to some of the parameters, such as the batch size, optimizer, and learning rate. Once the necessary code is written in train.py, the model can be trained by running the code in the terminal.
In this section, the speaker discusses the process of training a YOLOv5 model using Python and how to predict on a single image using the trained model. They load the dataset and show how to train the model using the optimizer and TensorBoard. The speaker then explains how to check for labeling errors in the dataset and discusses the different model weights that are generated during the training process. They then go on to demonstrate how to predict on a single image using the model and display the results.
In this section, the speaker showed how to plot the bounding boxes of the detected objects using res.plot(). They then converted the image from BGR to RGB format, saved it in PNG format, and demonstrated the detection of plastic waste using a different image as an example. The speaker recommended looking at the predict function documentation to explore additional functionalities. They also mentioned that YOLOv8 can handle various data formats for both images and videos and hinted at future videos covering segmentation and post tasks. Finally, the speaker encouraged viewers to like, subscribe, and share the video.
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