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A treasure chest for visual classification and recognition powered by PaddlePaddle
Official Pytorch implementation of CutMix regularizer
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)
An open-source toolkit which is full of handy functions, including the most used models and utilities for deep-learning practitioners!
Implementation of modern data augmentation techniques in TensorFlow 2.x to be used in your training pipeline.
Keras implementation of CutMix regularizer
Official Codes and Pretrained Models for RecursiveMix
FastClassification is a tensorflow toolbox for class classification. It provides a training module with various backbones and training tricks towards state-of-the-art class classification.
tensorflow2 implementation of SnapMix as described in SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
This is a data augmentation for object detection, using bounding boxes.
This is a TensorFlow implementation of the following paper: DropBlock: A regularization method for convolutional networks
Implementation of an advanced Convolutional Neural Network (CNN) for large-scale pest recognition, incorporating augmentation techniques and regularizers for improved accuracy and generalization.
Tensorflow2(Keras)のImageDataGeneratorのJupyter上での実行例。
Implementation of CutMix Augmentation with Keras.
Deep learning solution for Cassava Leaf Disease Classification, a Kaggle's Research Code Competition using Tensorflow.
Tensorflow2/KerasのImageDataGenerator向けのcutmixの実装。
DualDet implementation using PyTorch
Image Classification Using Swin Transformer With RandAugment, CutMix, and MixUp