Star

About Keras Getting started Developer guides Keras API reference Code examples Computer Vision Natural Language Processing Structured Data Timeseries Audio Data Generative Deep Learning Reinforcement Learning Graph Data Quick Keras Recipes Why choose Keras? Community & governance Contributing to Keras KerasTuner KerasCV KerasNLP

» Code examples / Computer Vision

Computer Vision

Image classification from scratch
Simple MNIST convnet
Image segmentation with a U-Net-like architecture
3D image classification from CT scans
Semi-supervision and domain adaptation with AdaMatch
Classification using Attention-based Deep Multiple Instance Learning (MIL).
Convolutional autoencoder for image denoising
Barlow Twins for Contrastive SSL
Image Classification using BigTransfer (BiT)
OCR model for reading Captchas
Compact Convolutional Transformers
Consistency training with supervision
Next-Frame Video Prediction with Convolutional LSTMs
Image classification with ConvMixer
CutMix data augmentation for image classification
Multiclass semantic segmentation using DeepLabV3+
Distilling Vision Transformers
Monocular depth estimation
Image classification with EANet (External Attention Transformer)
Enhanced Deep Residual Networks for single-image super-resolution
FixRes: Fixing train-test resolution discrepancy
Grad-CAM class activation visualization
Gradient Centralization for Better Training Performance
Handwriting recognition
Image Captioning
Image classification via fine-tuning with EfficientNet
Image classification with Vision Transformer
Model interpretability with Integrated Gradients
Involutional neural networks
Keypoint Detection with Transfer Learning
Knowledge Distillation
Learning to Resize in Computer Vision
Masked image modeling with Autoencoders
Metric learning for image similarity search
Metric learning for image similarity search using TensorFlow Similarity
Low-light image enhancement using MIRNet
MixUp augmentation for image classification
Image classification with modern MLP models
MobileViT: A mobile-friendly Transformer-based model for image classification
Near-duplicate image search
3D volumetric rendering with NeRF
Self-supervised contrastive learning with NNCLR
Object detection with Vision Transformers
Augmenting convnets with aggregated attention
Image classification with Perceiver
Point cloud classification with PointNet
Point cloud segmentation with PointNet
Investigating Vision Transformer representations
RandAugment for Image Classification for Improved Robustness
Few-Shot learning with Reptile
Object Detection with RetinaNet
Semantic Image Clustering
Semi-supervised image classification using contrastive pretraining with SimCLR
A Vision Transformer without Attention
Image similarity estimation using a Siamese Network with a contrastive loss
Image similarity estimation using a Siamese Network with a triplet loss
Self-supervised contrastive learning with SimSiam
Image Super-Resolution using an Efficient Sub-Pixel CNN
Supervised Contrastive Learning
Image classification with Swin Transformers
Learning to tokenize in Vision Transformers
Video Classification with a CNN-RNN Architecture
Video Classification with Transformers
Visualizing what convnets learn
Train a Vision Transformer on small datasets
Video Vision Transformer
Pneumonia Classification on TPU
Zero-DCE for low-light image enhancement