kp-algomaster Goto Github PK

aiml_healthcare

algomaster.github.io

HI everyone

android_image_processing

annotation_tools

attention_ocr_on_custom_data

avengers_chestxray_detection

boost-python-examples

Some examples for the use of boost::python

class_imbalance_solutions

data_preprocessing_with_python

detectron2

Detectron2 is FAIR's next-generation research platform for object detection and segmentation.

kp-algomaster.github.io

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mask_rcnn_demo

musegan

An AI for Music Generation

open-image-processing-tool

python38_cv

raspberry-pi-interfacing-with-mpu6050-motion-sensor

rudimentary-ai-composer

A very basic LSTM composer, doesn't compose any proper music for now

salesforcemobilesdk-android

Android SDK for Salesforce

semi-auto-image-annotation-tool

Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model

signal-processing

songbird.ai

A deep learning AI for generating music

structure_from_motion

3D reconstruction from Monocular vision

tensorflow_lite-experiments

tfjs_coverters

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

xgboost_with_python

yolox

As explained in YOLOX paper published by Zheng GE and Team, they have switched the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models. They have won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model.  Megvii researchers have cleverly integrated and combined outstanding progress in the field of object detection such as decoupling, data enhancement, anchorless and label classification with YOLO, and proposed YOLOX, which not only achieves AP that surpasses YOLOv3, YOLOv4 and YOLOv5 , but also achieved a very competitive reasoning speed. As this is very recent development in YOLO Series; one may face some issues while adapting this model on their custom dataset. In this post, we will walk through how you can train YOLOX to recognize object detection on your custom image Dataset.

yolox_rddc2020

YoloX Model training on RDDC2020 Dataset

yolox_training_custom_dataset

Megvii researchers have cleverly integrated and combined outstanding progress in the field of object detection such as decoupling, data enhancement, anchorless and label classification with YOLO, and proposed YOLOX, which not only achieves AP that surpasses YOLOv3, YOLOv4 and YOLOv5 , but also achieved a very competitive reasoning speed. As this is very recent development in YOLO Series; one may face some issues while adapting this model on their custom dataset. In this post, we will walk through how you can train YOLOX to recognize object detection on your custom image Dataset.