Share Split: Indian Sharing App to Transfer Files

Prateek Sawhney — Fri, 07 Aug 2020 16:06:23 +0000

Playstore- https://play.google.com/store/apps/details?id=net.sharesplit.android.android

Share Split app enables quick and easy file transfer without internet usage. Share Split app created by Prateek Sawhney is a better and easy to use sharing app with fast and stable transfer speeds. Share any kind of files including apps, games, obb files, photos, videos in a fast and efficient way. Share Split makes it easy to send files and apps to other devices in local network. Enjoy 100% seamless transfer of files and apps across various devices!

Transfer and Share in a few clicks ->

⭐ Share videos, music, photos, documents, obb files via android phones.
⭐ Ability to share multiple folders and large files with one single click.
⭐ Complete ad free experience and hassle free automatic fast transfer of apps and files.
⭐ Absolutely without any mobile data usage. No need to turn Internet off. One can use mobile data in between large transfers. Mobile data usage does not affect the transfer speed.
⭐ No need to turn your internet off! Yes, you read it right. With Share Split, data and apps can be sent smoothly across different platforms along with the usage of WiFi.
⭐ Send files and apps with Share Split from other android applications like Gallery, Google Docs and Google Photos.
⭐ Instant large file transfers and app transfers in the form of apk. Device apps are transferred in the form of apk. Send apk of the installed applications to other devices with the use of Share Split.
⭐ Option to become hidden in the local network.
⭐ Share history shows all the transfers performed including apps and files.
⭐ Indian Sharing Application - Made with ❤ in India

Why Share Split App?

⚡ Bulk Transfer Large Files
Look no further than Share Split when you need to transfer massive folders. You can share multiple large files and folders with one click!

⚡ Transfer files with flash speed
Send and receive any type of files, apps, music, games, obb files, documents, pdf's and more in no time. Transfer 200 times faster than Bluetooth.

⚡ No Ads
Enjoy a completely Ad free experience with no disruptions when sharing files. Receive files without any limitations. Receive contacts, apps, images, videos, music, documents, etc. easily.

⚡ No Loss in Quality
Send large files without any limitations and maintain the original file size. Send as much as you can with no loss in quality.

⚡ Only Storage Permission
In order to send and receive files, Share Split needs only the permission to read and write files on your Android device or the Storage Permission.

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[Support]
In case of any query, feel free to contact on: Prateek.sawhney97@gmail.com

Behavioral Cloning of Self Driving Car

Prateek Sawhney — Fri, 22 May 2020 08:54:22 +0000

Link to Code

https://github.com/prateeksawhney97/Behavioral-Cloning-Project-P4

My Final Project

Behavioral Cloning Project for Self-Driving Car Nano Degree Term 1. The project includes designing a neural network and then training the car on the road in unity simulator. The CNN learns and clones the driving behavior.

Demo Link

https://www.youtube.com/watch?v=2_6eNQr4yAc&feature=youtu.be

Steps followed:

The goals / steps of this project were the following:

Use the simulator to collect data of good driving behavior
Build, a convolution neural network in Keras that predicts steering angles from images
Train and validate the model with a training and validation set
Test that the model successfully drives around track one without leaving the road

First of all, the model is trained to generate the model.h5 file with the help of following command. model.py file contains the code to train the model.

python model.py

Using the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing:

python drive.py model.h5

After the car successfully steers through the track, the video of the driving behavior can be formed by producing various frames and saving that frames in the output-video folder, by executing the following command. The fourth argument, output-video, is the directory in which to save the images seen by the agent. If the directory already exists, it'll be overwritten.

python drive.py model.h5 output-video

After all the frames of the car driving in the simulator are saved in the output-video folder, the video can be made by combining all the frames with the use of following command. It creates a video based on images found in the output-video directory. The name of the video will be the name of the directory followed by '.mp4'.

python video.py output-video

Optionally, we can specify the FPS (frames per second) of the video. The default is 60 fps.

python video.py output-video --fps 48

Model Architecture

My model consists of a convolution neural network which is implemented with the help of keras in a much easier way. The model is like the NVIDIA model, and contains five Convolutional layers and four Dense layers. The model also contains a Dropout layer, a Flatten layer and one Cropping2D layer. The data is normalized in the model using a Keras lambda layer. The total number of parameters in the proposed model is 348, 219.

Attempts to reduce overfitting in the model

The model contains dropout layer in order to reduce overfitting. There is a Dropout layer after the five Convolutional Layers to reduce overfitting. The Dropout Layer has a probability of 0.5 to dropout the weights. The model was trained and validated on different data sets to ensure that the model was not overfitting. The model was tested by running it through the simulator and ensuring that the vehicle could stay on the track.

Model Parameter Tuning

The model uses an Adam optimizer, so the learning rate is tuned manually. "optimizer=Adam(lr=1.0e-4)" depicts the usage of Adam optimizer with a learning rate of "1.0e-4". The number of epochs is set to 10 and batch_size is set to 32.

Appropriate Training Data

Training data was chosen to keep the vehicle driving on the road. I used training data by driving for around three tracks on the road. Nearly 13,000 images including the center, left and right camera images were used to train the model. Various training Data Augmentation techniques were used to augment the training data like random flip, random translate, random brightness and RGB to YUV image conversion just as NVIDIA uses in its model.

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