About Keras Getting started Developer guides Code examples Computer Vision Image classification from scratch Simple MNIST convnet Image classification via fine-tuning with EfficientNet Image classification with Vision Transformer 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 Classification using Global Context Vision Transformer When Recurrence meets Transformers Image segmentation with a U-Net-like architecture Multiclass semantic segmentation using DeepLabV3+ Highly accurate boundaries segmentation using BASNet Image Segmentation using Composable Fully-Convolutional Networks 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 segmentation with PointNet 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 Self-supervised contrastive learning with NNCLR Video Classification with a CNN-RNN Architecture Next-Frame Video Prediction with Convolutional LSTMs Video Classification with Transformers Video Vision Transformer Image Classification using BigTransfer (BiT) Gradient Centralization for Better Training Performance Learning to tokenize in Vision Transformers Knowledge Distillation FixRes: Fixing train-test resolution discrepancy Class Attention Image Transformers with LayerScale Augmenting convnets with aggregated attention Learning to Resize Semi-supervision and domain adaptation with AdaMatch Barlow Twins for Contrastive SSL Consistency training with supervision Distilling Vision Transformers Focal Modulation: A replacement for Self-Attention Using the Forward-Forward Algorithm for Image Classification Masked image modeling with Autoencoders Segment Anything Model with 🤗Transformers Semantic segmentation with SegFormer and Hugging Face Transformers Self-supervised contrastive learning with SimSiam Supervised Contrastive Learning 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 Keras 3 API documentation Keras 2 API documentation KerasTuner: Hyperparam Tuning KerasHub: Pretrained Models