Computer Vision
Deep dives into computer vision: object detection, segmentation, image captioning, CNNs, and beyond.
18 posts
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Computer VisionPyTorch Basic Tutorial
A practical introduction to PyTorch covering tensors, autograd, neural network modules, and key libraries like torchvision and torchaudio.
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Computer VisionColor and color spaces in Computer Vision
Understanding color models (RGB, HSV, LAB) and color spaces in computer vision, how computers represent and work with color.
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Deep LearningIntroduction to Panoptic Segmentation: A Tutorial
Panoptic segmentation unifies semantic and instance segmentation assigning class labels and unique IDs to every pixel in an image.
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Deep LearningEvaluation metrics for object detection and segmentation: mAP
How IoU, precision-recall curves, and mean Average Precision (mAP) are used to evaluate object detection and segmentation models.
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Deep LearningQuick intro to Instance segmentation: Mask R-CNN
Instance segmentation with Mask R-CNN: combining object detection and semantic segmentation to identify and segment each object instance separately.
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Deep LearningQuick intro to semantic segmentation: FCN, U-Net and DeepLab
An introduction to semantic segmentation, pixel-level classification using Fully Convolutional Networks, U-Net, and DeepLab architectures.
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Deep Learning
Converting FC layers to CONV layers
How and why to replace fully connected layers with equivalent convolutional layers, enabling CNNs to accept arbitrary input sizes.
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Deep LearningData augmentation
How data augmentation like flipping, rotation, color jittering artificially expands training data to build more generalizable deep learning models.
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Deep LearningGenerative Adversarial Networks variants: DCGAN, Pix2pix, CycleGAN
An overview of GAN variants, DCGAN for image generation, Pix2pix for paired image translation, and CycleGAN for unpaired style transfer.
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Deep LearningQuick intro to Object detection: R-CNN, YOLO, and SSD
A concise introduction to object detection methods, classification with localization, R-CNN family, YOLO, and SSD.
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Deep LearningAttention
The attention mechanism in sequence-to-sequence models, how it allows the decoder to focus on relevant parts of the input at each step.
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Deep LearningImage captioning using encoder-decoder
Building an image captioning system using a CNN encoder and RNN decoder based on the Show and Tell architecture.
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Deep LearningWhy Batch Normalization?
How batch normalization speeds up training by normalizing hidden layer activations across the network using learnable scale and shift parameters.
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Deep LearningFilters in Convolutional Neural Networks
How convolutional filters detect spatial patterns and edges by responding to high-frequency changes in image pixel intensity.
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Deep LearningGenerative models and Generative Adversarial Networks
An introduction to generative models and GANs, how a generator and discriminator compete to produce realistic synthetic data.
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Deep LearningSkip connections and Residual blocks
How ResNet's skip connections and residual blocks solve the degradation problem in very deep neural networks.
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Deep Learning
Transfer learning: How to build accurate models
Using pre-trained CNN models via feature extraction or fine-tuning to build accurate models when training data is limited.
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Deep LearningThe magic behind ConvNets
How Convolutional Neural Networks work: convolutional, pooling, and fully connected layers, and how features are extracted from images.