esla

Class-balanced-loss-pytorch

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

commons-csv

confidence-aware-learning

Confidence-Aware Learning for Deep Neural Networks (ICML2020)

CutMix-PyTorch

Official Pytorch implementation of CutMix regularizer

cvplayer

The easiest way to visualize your computer vision models inferences.

DeepVideoAnalytics

A distributed visual search and visual data analytics platform.

Detectron2_AL

Active Learning for Object Detection using Detectron2

Multilabelfy

Multilabelfy: A straightforward framework designed to enhance datasets that are inherently multi-label but have been represented as single-label.

qa-test-definitions

trypanosome_parasite_detection

Accelerate research in trypanosome detection with this code repository. Train and evaluate object detectors using state-of-the-art architectures, accompanied by preprocessing utilities, training scripts, and evaluation metrics. Harness the power of deep learning for accurate trypanosome parasite detection.

esla/qa-test-definitions

esla/Class-balanced-loss-pytorch

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

esla/commons-csv

esla/confidence-aware-learning

Confidence-Aware Learning for Deep Neural Networks (ICML2020)

esla/CutMix-PyTorch

Official Pytorch implementation of CutMix regularizer

esla/cvplayer

The easiest way to visualize your computer vision models inferences.

esla/DeepVideoAnalytics

A distributed visual search and visual data analytics platform.

esla/Detectron2_AL

Active Learning for Object Detection using Detectron2

esla/example_forgetting

esla/focal_calibration

Code for the paper "Calibrating Deep Neural Networks using Focal Loss"

esla/Multilabelfy

Multilabelfy: A straightforward framework designed to enhance datasets that are inherently multi-label but have been represented as single-label.

esla/trypanosome_parasite_detection

Accelerate research in trypanosome detection with this code repository. Train and evaluate object detectors using state-of-the-art architectures, accompanied by preprocessing utilities, training scripts, and evaluation metrics. Harness the power of deep learning for accurate trypanosome parasite detection.

esla/hacking-sessions

esla/minilava

Salt Configuration for my mini LAVA lab

esla/podam

PODAM - POjo DAta Mocker

esla/pytorch_image_classification

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

esla/Reassessing_DNN_Accuracy

esla/test-definitions

esla/tests

esla/yolov7

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors