About Keras Getting started Code examples Computer Vision Image classification from scratch Simple MNIST convnet Image classification via fine-tuning with EfficientNet Image classification with Vision Transformer Image Classification using BigTransfer (BiT) Classification using Attention-based Deep Multiple Instance Learning Image classification with modern MLP models A mobile-friendly Transformer-based model for image classification Pneumonia Classification on TPU Compact Convolutional Transformers Image classification with ConvMixer Image classification with EANet (External Attention Transformer) Involutional neural networks Image classification with Perceiver Few-Shot learning with Reptile Semi-supervised image classification using contrastive pretraining with SimCLR Image classification with Swin Transformers Train a Vision Transformer on small datasets A Vision Transformer without Attention Image segmentation with a U-Net-like architecture Multiclass semantic segmentation using DeepLabV3+ Highly accurate boundaries segmentation using BASNet Object Detection with RetinaNet Keypoint Detection with Transfer Learning Object detection with Vision Transformers 3D image classification from CT scans Monocular depth estimation 3D volumetric rendering with NeRF Point cloud classification OCR model for reading Captchas Handwriting recognition Convolutional autoencoder for image denoising Low-light image enhancement using MIRNet Image Super-Resolution using an Efficient Sub-Pixel CNN Enhanced Deep Residual Networks for single-image super-resolution Zero-DCE for low-light image enhancement CutMix data augmentation for image classification MixUp augmentation for image classification RandAugment for Image Classification for Improved Robustness Image captioning Natural language image search with a Dual Encoder Visualizing what convnets learn Model interpretability with Integrated Gradients Investigating Vision Transformer representations Grad-CAM class activation visualization Near-duplicate image search Semantic Image Clustering Image similarity estimation using a Siamese Network with a contrastive loss Image similarity estimation using a Siamese Network with a triplet loss Metric learning for image similarity search Metric learning for image similarity search using TensorFlow Similarity Video Classification with a CNN-RNN Architecture Next-Frame Video Prediction with Convolutional LSTMs Video Classification with Transformers Video Vision Transformer Semi-supervision and domain adaptation with AdaMatch Barlow Twins for Contrastive SSL Class Attention Image Transformers with LayerScale Consistency training with supervision Distilling Vision Transformers FixRes: Fixing train-test resolution discrepancy Focal Modulation: A replacement for Self-Attention Using the Forward-Forward Algorithm for Image Classification Image Segmentation using Composable Fully-Convolutional Networks Gradient Centralization for Better Training Performance Knowledge Distillation Learning to Resize in Computer Vision Masked image modeling with Autoencoders Self-supervised contrastive learning with NNCLR Augmenting convnets with aggregated attention Point cloud segmentation with PointNet Semantic segmentation with SegFormer and Hugging Face Transformers Self-supervised contrastive learning with SimSiam Supervised Contrastive Learning When Recurrence meets Transformers Learning to tokenize in Vision Transformers Efficient Object Detection with YOLOV8 and KerasCV Natural Language Processing Structured Data Timeseries Generative Deep Learning Audio Data Reinforcement Learning Graph Data Quick Keras Recipes Developer guides API reference Keras Core: Keras for TensorFlow, JAX, and PyTorch KerasTuner: Hyperparameter Tuning KerasCV: Computer Vision Workflows KerasNLP: Natural Language Workflows Why choose Keras? Community & governance Contributing to Keras