Kushagra343's Stars
pyeve/eve-oauth2
Example of a Eve powered API secured with Flask-Sentinel
amiv-eth/amivapi
The REST API behind most of AMIV's web services.
jay3dec/REST_API_EVE_Part-1
Nitinguptadu/RSNA-Pneumonia-Detection-Challenge
In this competition, you’re challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.
Nitinguptadu/Faster-R-CNN-for-Open-Images-Dataset-by-Keras
Introduction The original code of Keras version of Faster R-CNN I used was written by yhenon (resource link: GitHub .) He used the PASCAL VOC 2007, 2012, and MS COCO datasets. For me, I just extracted three classes, “Person”, “Car” and “Mobile phone”, from Google’s Open Images Dataset V4. I applied configs different from his work to fit my dataset and I removed unuseful code. Btw, to run this on Google Colab (for free GPU computing up to 12hrs), I compressed all the code into three .ipynb notebooks. Sorry for the messy structure. I wrote my exploring and experiment results for Faster R-CNN in this article in Medium. If you are in China, you cannot directly access Medium. So I make a copy in here. Project Structure Object_Detection_DataPreprocessing.ipynb is the file to extract subdata from Open Images Dataset V4 which includes downloading the images and creating the annotation files for our training. I run this part by my own computer because of no need for GPU computation. frcnn_train_vgg.ipynb is the file to train the model. The configuration and model saved path are inside this file. frcnn_test_vgg.ipynb is the file to test the model with test images and calculate the mAP (mean average precision) for the model.
Nitinguptadu/Faster-R-CNN-model-deployment-using-flask-on-local-host
In this Repository I have deployed the faster Rcnn model on flask to deploy it on local server and aslo provided the code for how to use AWS sagamaker to deploy your pre train model on aws
Nitinguptadu/Faster-R-CNN-model-dockerization
Nitinguptadu/Pillow-image-data-augumenatation-using-manullay-preprocessing
I also noticed there are lots of images in the data which are specific B&W or only of R/B/G channel. Based on these observations I decided to write the below code to do small changes in images which are from unbalanced classes in training sample ans save them:
Nitinguptadu/Cifar-10-data-set-with-train-accuracy-99-and-test-accuracy-93-
This repo is for self learning purpose
Nitinguptadu/Faster-Rcnn-docker-
Nitinguptadu/Image-Classifiaction-Using-Resnet-
This Model Runs on Free Heroku server .Static page is inside app.py . Model is loaded by keras application
Nitinguptadu/Image-classification-Heroku-
This code is for self learning purpose
bhanuvrat/popular-web-frameworks
Curating a list of popular open-source web frameworks.
cozitsphysics/Faster-R-CNN-model-dockerization
cozitsphysics/PyTorch-SSD
All code was taken from Max deGroot's & Ellis Brown's ssd.pytorch repository except the object_detection.py file. However, some modifications were done in order to make this project run on Windows 10 and Python 3.6 with PyTorch 0.4.1
cozitsphysics/RSNA-Pneumonia-Detection-Challenge
In this competition, you’re challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.
cozitsphysics/Yolo-V-3-network-from-scratch-in-pytorch
yolo v3 in pytorch ( python version 3 ) for image real time image detection and video detection (video formate .avi supported by opencv )
Kushagra343/Yolo-V-3-network-from-scratch-in-pytorch
yolo v3 in pytorch ( python version 3 ) for image real time image detection and video detection (video formate .avi supported by opencv )