â–º Code examples / Computer Vision

Computer Vision

Image classification

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Image classification from scratch
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Simple MNIST convnet
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Image classification via fine-tuning with EfficientNet
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Image classification with Vision Transformer
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Classification using Attention-based Deep Multiple Instance Learning
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Image classification with modern MLP models
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A mobile-friendly Transformer-based model for image classification
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Pneumonia Classification on TPU
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Compact Convolutional Transformers
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Image classification with ConvMixer
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Image classification with EANet (External Attention Transformer)
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Involutional neural networks
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Image classification with Perceiver
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Few-Shot learning with Reptile
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Semi-supervised image classification using contrastive pretraining with SimCLR
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Image classification with Swin Transformers
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Train a Vision Transformer on small datasets
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A Vision Transformer without Attention
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Image Classification using Global Context Vision Transformer
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When Recurrence meets Transformers
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Image Classification using BigTransfer (BiT)

Image segmentation

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Image segmentation with a U-Net-like architecture
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Multiclass semantic segmentation using DeepLabV3+
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Highly accurate boundaries segmentation using BASNet
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Image Segmentation using Composable Fully-Convolutional Networks

Object detection

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Object Detection with RetinaNet
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Keypoint Detection with Transfer Learning
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Object detection with Vision Transformers

3D

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3D image classification from CT scans
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Monocular depth estimation
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3D volumetric rendering with NeRF
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Point cloud segmentation with PointNet
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Point cloud classification

OCR

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OCR model for reading Captchas
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Handwriting recognition

Image enhancement

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Convolutional autoencoder for image denoising
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Low-light image enhancement using MIRNet
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Image Super-Resolution using an Efficient Sub-Pixel CNN
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Enhanced Deep Residual Networks for single-image super-resolution
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Zero-DCE for low-light image enhancement

Data augmentation

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CutMix data augmentation for image classification
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MixUp augmentation for image classification
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RandAugment for Image Classification for Improved Robustness

Image & Text

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Image captioning
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Natural language image search with a Dual Encoder

Vision models interpretability

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Visualizing what convnets learn
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Model interpretability with Integrated Gradients
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Investigating Vision Transformer representations
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Grad-CAM class activation visualization

Image similarity search

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Near-duplicate image search
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Semantic Image Clustering
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Image similarity estimation using a Siamese Network with a contrastive loss
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Image similarity estimation using a Siamese Network with a triplet loss
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Metric learning for image similarity search
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Metric learning for image similarity search using TensorFlow Similarity
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Self-supervised contrastive learning with NNCLR

Video

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Video Classification with a CNN-RNN Architecture
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Next-Frame Video Prediction with Convolutional LSTMs
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Video Classification with Transformers
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Video Vision Transformer

Performance recipes

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Gradient Centralization for Better Training Performance
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Learning to tokenize in Vision Transformers
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Knowledge Distillation
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FixRes: Fixing train-test resolution discrepancy
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Class Attention Image Transformers with LayerScale
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Augmenting convnets with aggregated attention
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Learning to Resize

Other

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Semi-supervision and domain adaptation with AdaMatch
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Barlow Twins for Contrastive SSL
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Consistency training with supervision
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Distilling Vision Transformers
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Focal Modulation: A replacement for Self-Attention
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Using the Forward-Forward Algorithm for Image Classification
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Masked image modeling with Autoencoders
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Segment Anything Model with 🤗Transformers
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Semantic segmentation with SegFormer and Hugging Face Transformers
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Self-supervised contrastive learning with SimSiam
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Supervised Contrastive Learning
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Efficient Object Detection with YOLOV8 and KerasCV