Challenge: Apply advanced computer vision concepts (CNN only) and beat state-of-the-art using constrained resources and concepts.
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Updated
Jun 21, 2021 - Jupyter Notebook
Challenge: Apply advanced computer vision concepts (CNN only) and beat state-of-the-art using constrained resources and concepts.
Code and final submission for the Tiny ImageNet Challenge. Trained on Google colab and finished top 5 among 1072 participants
Tensorflow implementation of Image Matching with Triplet Loss on the Tiny ImageNet dataset.
Image Classification Training Framework for Network Distillation
Merged Geometrical Homogeneous Clustering for Image Data Reduction. An algorithm to reduce large image datasets maintaining similar accuracy.
Official PyTorch Implementation of Guarding Barlow Twins Against Overfitting with Mixed Samples
Official PyTorch Implementation for the "Distilling Datasets Into Less Than One Image" paper.
A Steganography model that uses Deep Learning in order to hide secret images within covers, making it impossible to be deciphered by the naked eye.
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".
Implémentation du papier Colorization Transformer (ICLR 2021) - Version Expérimentale
mini-imagenet and tiny-imagent dataset transformation for traditional classification task and also for the format for few-shot learning / meta-learning tasks
Image Tagger: AI-based Android App for Automated Image Annotation
An implementation of MobileNetV3 with pyTorch
Image classification on Tiny ImageNet
PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017
🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
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