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
search
â–º
Code examples
/ Computer Vision
Computer Vision
Image classification
★
V3
Image classification from scratch
★
V3
Simple MNIST convnet
★
V3
Image classification via fine-tuning with EfficientNet
V3
Image classification with Vision Transformer
V3
Classification using Attention-based Deep Multiple Instance Learning
V3
Image classification with modern MLP models
V3
A mobile-friendly Transformer-based model for image classification
V3
Pneumonia Classification on TPU
V3
Compact Convolutional Transformers
V3
Image classification with ConvMixer
V3
Image classification with EANet (External Attention Transformer)
V3
Involutional neural networks
V3
Image classification with Perceiver
V3
Few-Shot learning with Reptile
V3
Semi-supervised image classification using contrastive pretraining with SimCLR
V3
Image classification with Swin Transformers
V3
Train a Vision Transformer on small datasets
V2
A Vision Transformer without Attention
V3
Image Classification using Global Context Vision Transformer
V3
When Recurrence meets Transformers
V3
Image Classification using BigTransfer (BiT)
Image segmentation
★
V3
Image segmentation with a U-Net-like architecture
V3
Multiclass semantic segmentation using DeepLabV3+
V2
Highly accurate boundaries segmentation using BASNet
V3
Image Segmentation using Composable Fully-Convolutional Networks
Object detection
V2
Object Detection with RetinaNet
V3
Keypoint Detection with Transfer Learning
V3
Object detection with Vision Transformers
3D
V3
3D image classification from CT scans
V3
Monocular depth estimation
★
V3
3D volumetric rendering with NeRF
V3
Point cloud segmentation with PointNet
V3
Point cloud classification
OCR
V3
OCR model for reading Captchas
V3
Handwriting recognition
Image enhancement
V3
Convolutional autoencoder for image denoising
V3
Low-light image enhancement using MIRNet
V3
Image Super-Resolution using an Efficient Sub-Pixel CNN
V3
Enhanced Deep Residual Networks for single-image super-resolution
V3
Zero-DCE for low-light image enhancement
Data augmentation
V3
CutMix data augmentation for image classification
V3
MixUp augmentation for image classification
V3
RandAugment for Image Classification for Improved Robustness
Image & Text
★
V3
Image captioning
V2
Natural language image search with a Dual Encoder
Vision models interpretability
V3
Visualizing what convnets learn
V3
Model interpretability with Integrated Gradients
V3
Investigating Vision Transformer representations
V3
Grad-CAM class activation visualization
Image similarity search
V2
Near-duplicate image search
V3
Semantic Image Clustering
V3
Image similarity estimation using a Siamese Network with a contrastive loss
V3
Image similarity estimation using a Siamese Network with a triplet loss
V3
Metric learning for image similarity search
V2
Metric learning for image similarity search using TensorFlow Similarity
V3
Self-supervised contrastive learning with NNCLR
Video
V3
Video Classification with a CNN-RNN Architecture
V3
Next-Frame Video Prediction with Convolutional LSTMs
V3
Video Classification with Transformers
V3
Video Vision Transformer
Performance recipes
V3
Gradient Centralization for Better Training Performance
V3
Learning to tokenize in Vision Transformers
V3
Knowledge Distillation
V3
FixRes: Fixing train-test resolution discrepancy
V3
Class Attention Image Transformers with LayerScale
V3
Augmenting convnets with aggregated attention
V3
Learning to Resize
Other
V2
Semi-supervision and domain adaptation with AdaMatch
V2
Barlow Twins for Contrastive SSL
V2
Consistency training with supervision
V2
Distilling Vision Transformers
V2
Focal Modulation: A replacement for Self-Attention
V2
Using the Forward-Forward Algorithm for Image Classification
V2
Masked image modeling with Autoencoders
V2
Segment Anything Model with 🤗Transformers
V2
Semantic segmentation with SegFormer and Hugging Face Transformers
V2
Self-supervised contrastive learning with SimSiam
V2
Supervised Contrastive Learning
V2
Efficient Object Detection with YOLOV8 and KerasCV