jkjung-avt

dqn-tx1-for-nintendo

My attempt to run DQN on Jetson TX1, which learns how to play Nintendo Famicom Mini games through reinforcement learning directly.

hand-detection-tutorial

A tutorial on how to train a hand detector with TensorFlow Object Detection API

jetson_nano

This repository is a collection of scripts/programs I use to set up the software development environment on my Jetson Nano, TX2, and Xavier NX.

keras-cats-dogs-tutorial

A practical example of image classifier with Keras 2.x and TensorFlow backend, using the Kaggle Cats vs. Dogs dataset. By taking advantage of Keras' image data augmentation capabilities (and also random cropping), we were able to achieve 99% accuracy on the trained model with only 2,000 images in the training set.

keras_imagenet

Code for training Keras ImageNet (ILSVRC2012) image classification models from scratch

tensorrt_demos

TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet

tf_trt_models

TensorFlow object detection models accelerated with NVIDIA TensorRT (TF-TRT)

yolov4_crowdhuman

A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset

jkjung-avt/tensorrt_demos

TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet

jkjung-avt/jetson_nano

This repository is a collection of scripts/programs I use to set up the software development environment on my Jetson Nano, TX2, and Xavier NX.

jkjung-avt/hand-detection-tutorial

A tutorial on how to train a hand detector with TensorFlow Object Detection API

jkjung-avt/yolov4_crowdhuman

A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset

jkjung-avt/tf_trt_models

TensorFlow object detection models accelerated with NVIDIA TensorRT (TF-TRT)

jkjung-avt/keras-cats-dogs-tutorial

A practical example of image classifier with Keras 2.x and TensorFlow backend, using the Kaggle Cats vs. Dogs dataset. By taking advantage of Keras' image data augmentation capabilities (and also random cropping), we were able to achieve 99% accuracy on the trained model with only 2,000 images in the training set.

jkjung-avt/keras_imagenet

Code for training Keras ImageNet (ILSVRC2012) image classification models from scratch

jkjung-avt/CudaMiner

a CUDA accelerated litecoin mining application for NVIDIA Jetson TX2/TX1

jkjung-avt/dqn-tx1-for-nintendo

My attempt to run DQN on Jetson TX1, which learns how to play Nintendo Famicom Mini games through reinforcement learning directly.

jkjung-avt/keras-yolo3

A Keras implementation of YOLOv3 (TensorFlow backend), which I used to train an object detector for the Kaggle 2018 "Google AI Open Images - Object Detection Track" competition

jkjung-avt/jkjung-avt.github.io

My blog posts

jkjung-avt/DeepMind-Atari-Deep-Q-Learner

DeepMind's DQN code + my tweaks (works on NVIDIA Jetson TX1)

jkjung-avt/darknet

YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )

jkjung-avt/py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

jkjung-avt/caffe_merge_batchnorm

My repository for testing how to merge/remove BatchNorm and Scale layers in trained caffe models

jkjung-avt/dataset-tools

A collection of tools/scripts I use to process deep learning datasets

jkjung-avt/nv-caffe

Caffe: a fast open framework for deep learning.

jkjung-avt/DeepLens_Notifier

An AWS DeepLens agent which sends a LINE notifier to me when it detects an event

jkjung-avt/Asset-Rx-Edge-Container-Tutorial

Welcome to the Tutorial for making your first Asset-Rx Edge Containers~

jkjung-avt/deeplearning-cats-dogs-tutorial

A Practical Introduction to Deep Learning with Caffe and Python (works on Jetson TX2)

jkjung-avt/dockerfiles

jkjung-avt/yolov7

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

jkjung-avt/llama2.c

Inference Llama 2 in one file of pure C

jkjung-avt/LodeRunner_TotalRecall

Lode Runner - Total Recall (超級**員-全面回憶) : Remake in HTML5

jkjung-avt/nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.