DualDet implementation using PyTorch
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Updated
Feb 27, 2023 - Python
DualDet implementation using PyTorch
To evaluate the performance of each regularization method (cutout, mixup, and self-supervised rotation predictor), we apply it to the CIFAR-10 dataset using a deep residual network with a depth of 20 (ResNet20)
Python codes to implement DeMix, a DETR assisted CutMix method for image data augmentation
HTCLite implementation using PyTorch (supports MOSAIC/MixUp and RandomAugment)
Classification using Vision Transformers (ViT) and MixUp Augmentation
[IJCAI 2023] Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Code for Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multiagent Trajectory Prediction (TNNLS 2022)
Image Classification Using Swin Transformer With RandAugment, CutMix, and MixUp
Faster large mini-batch distributed training w/o. squeezing devices
A new regularization technique by encountering samples through exponential smoothing
High Accuracy ResNet Model under 5 Million parameters.
A repository to host recent papers on Manifold Mixup.
Deep learning solution for Cassava Leaf Disease Classification, a Kaggle's Research Code Competition using Tensorflow.
Bronze medal solution for the "Bengali.AI Handwritten Grapheme Classification" Kaggle competition
Official implementation of Any Region Can Be Perceived Equally and Effectively on Rotation Pretext Task Using Full Rotation and Weighted-Region Mixture
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