CNN
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
With millions of trainable parameters, neural networks have long been considered black boxes. They can produce stunning results, and we often accept the output with very little understanding as to
Medical diagnostics rely on quick, precise image classification. Using PyTorch & Lightning, we fine-tune EfficientNetv2 for medical multi-label classification.
In this article, we use ImageNet pre-trained CNN models for image classification tasks.
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 post, we’ll learn how to implement a Convolutional Neural Network (CNN) from scratch using Keras. Here, we show a CNN architecture similar to the structure of VGG-16 but
FCOS: Fully Convolutional One-stage Object Detection is an anchor-free (anchorless) object detector. Inference on image and video with PyTorch.
YOLOv7 Pose is a real time, multi-person, keypoint detection model capable of giving highly accurate pose estimation results.
YOLOX object detector is a recent addition in the YOLO family. Read the article for detailed YOLOX paper explanation and learn how to train YOLOX on a custom dataset.
Moving away from traditional document scanners, learn how to create a Deep Learning-based Document Segmentation model using DeepLabv3 architecture in PyTorch.
Mean Average Precision (mAP) is a performance metric used for evaluating machine learning models. We have covered mAP evaluation in detail to clear all your confusions regarding model evaluation metrics.
A technical review of YOLOv7 paper along with inference report. YOLOv7 Pose detection code included.
