Image Classification
The field of computer vision is fueled by the remarkable progress in self-supervised learning. At the forefront of this revolution is DINOv2, a cutting-edge self-supervised vision transformer developed by Meta
In the rapidly evolving field of deep learning, the challenge often lies not just in designing powerful models but also in making them accessible and efficient for practical use, especially
In this article, we explore the real-time facial emotion recognition using the RFB-320 SSD face detection model and the VGG-13 emotion recognition model.
Medical diagnostics rely on quick, precise image classification. Using PyTorch & Lightning, we fine-tune EfficientNetv2 for medical multi-label classification.
In this article, we explore the technique of fine-tuning pretrained models on custom datasets.
This article discusses the working of Convolutional Neural Networks on depth for image classification along with diving deeper into the detailed operations of CNN.
In this article, we explore the Ultralytics YOLOv8 models for object detection, instance segmentation, and image classification.
In this article, we cover the basics of training neural networks for beginners for an image classification problem.
In the previous posts of the TFLite series, we introduced TFLite and the process of creating a model. In this post, we will take a deeper dive into the TensorFlow
In this article, we will learn how to create a TensorFlow Lite model using the TF Lite Model Maker Library. We will fine-tune a pre-trained image classification model on the
The recent trend in developing larger and larger Deep Learning models for a slight increase in accuracy raises concerns about their computational efficiency and wide scaled usability. We can not
In the previous post, we’ve learned how to work with OpenCV Java API with the example of a PyTorch convolutional neural network, integrated into the Java pipeline. Now we are going
