Faster R-CNN for Open Images Dataset by Keras

A hardware implementation of CNN, written by Verilog and synthesized on FPGA

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

Squeezenet V1.1 on Cyclone V SoC-FPGA at 450ms/image, 20x faster than ARM A9 processor alone. A project for 2017 Innovate FPGA design contest.

An Attention Based Open-Source End to End Speech Synthesis Framework, No CNN, No RNN, No MFCC!!!

OpenCL HLS based CNN Accelerator on Intel DE10 Nano FPGA.

A Transfer Learning based Object Detection API that detects all objects in an image, video or live webcam. An SSD model and a Faster R-CNN model was pretrained on Mobile net coco dataset along with a label map in Tensorflow. This model were used to detect objects captured in an image, video or real time webcam. Open CV was used for streaming objects and preprocessing.

An Open Convolutional Neural Network Framework in C++ From Scratch

Open-Source Sidewalk Navigation Software for Visually-Impaired Individuals using Multithreaded CNN’s

Implementation of "MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving" for Waymo Open Motion Dataset

基于改进CNN和OpenCV的行人轨迹实时预测系统（源码＆教程）

Here, I created my own deep learning(CNN) model for early detection of COVID-19 from chest x-ray images. If we were to answer the question that why we need a deep learning model for early detection of COVID-19 from chest x-ray images, we can say the followings, doctors have seen that even if the test kits desined for diagnosis results in negative, the real results are positive for some patients when they review the chest X-ray images. For now the public dataset contains less amount of data which you can see in the dataset2 folder. We get this dataset from open-source https://github.com/ieee8023/covid-chestxray-dataset, but for sure it is not enough to train a proper deep learning model. But just to show that how easy it is to create an AI for the early detection of these kind of viruses. Just keep in mind that this cannot be used for diagnosis without training many more images in high-resolution and professinal medical tests. There you go! Let's work together to fight against COVID-19. As a tool, I used Keras with Tensorflow background, and the model can be improved by addig more convolution and pooling layers, and increasing the number of feature detectors'. Don't forget to upvote. Best Regards.

The power of machine learning allows us to change long-standing computing paradigms. One of these is the age-old password-based authentication system common to most apps. With fast real-time facial recognition, we can easily dispense with text-based verification and allow users to log in just by showing their faces to a webcam. In this session, we’ll show how to do this in Flutter, Google’s popular open-source UI toolkit for developing apps for web, Android, iOS, Fuchsia, and many other platforms with a single codebase. We’ll first build a simple authentication-based Android app, and then deploy the Firebase ML Vision model for face ID & image processing; as well as the MobileFaceNet CNN model through TensorFlow Lite for structured verification. Once all these parts are in place, our solution will work seamlessly and can easily be ported to other apps. Pre-requisites: ✅ Android Studio (https://developer.android.com/studio) — you can also use other IDEs/platforms if you’d rather not use Android - Flutter documentation below guides on the same. ✅ Flutter SDK (https://flutter.dev/docs/get-started/install) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #TensorFlow #Flutter #MachineLearning

opencv+CNN实现实时手写数字识别

A Winograd Minimal Filter Implementation in CUDA

Guide : Prof Sundaram Suresh (NTU- Singapore) Area: Deep learning neural networks for feature extraction in high dimensional neuro imaging data. Tools used: Standard neuro imaging software for preprocessing, a MATLAB deep learning toolbox DeeBNet. I used deep learning algorithms including RBM’s and CNN’s to train on an open source MRI data set and classify unseen fMRI scans as having ADHD or not. I was able to achieve accuracy scores of 64% which is incrementally better than the current start of art(as of 2016). The project was challenging due to the high dimensionality of the input data and the meager number of test samples.

This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.

Open source Artificial Intelligence for COVID-19 detection/early detection. Includes Convolutional Neural Networks (CNN) & Generative Adversarial Networks (GAN)

An open source Verilog Based LeNet-1 Parallel CNNs Accelerator for FPGAs in Vivado 2017

With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general-purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalizability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML (Automated Machine Learning), would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general-purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. To address these problems, in this project, as part of the MSD challenge, we propose a generic machine learning algorithm which we applied on two organs: liver and tumors, spleen. We propose an unsupervised generic model by implementing U-net CNN architecture with Generalized Dice Coefficient as loss function and also as a metric. The MSD dataset consists of dozens of medical examinations in 3D (per organ), we’ll transform the 3-dimensional data into 2-d cuts as an input of our U-net. Experimental results show that our generic model based on U-net and Generalized Dice Coefficient algorithm leads to high segmentation accuracy for each organ (liver and tumors, spleen), separately, without human interaction, with a relatively short run time compared to traditional segmentation methods.

This code helps you to classify digits using OpenCV and CNN

An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation

Who doesn’t dream of a new FPGA family that can provide embedded hard neurons in its silicon architecture fabric instead of the conventional DSP and multiplier blocks? The optimized hard neuron design will allow all the software and hardware designers to create or test different deep learning network architectures, especially the convolutional neural networks (CNN), more easily and faster in comparing to any previous FPGA family in the market nowadays. The revolutionary idea about this project is to open the gate of creativity for a precise-tailored new generation of FPGA families that can solve the problems of wasting logic resources and/or unneeded buses width as in the conventional DSP blocks nowadays. The project focusing on the anchor point of the any deep learning architecture, which is to design an optimized high-speed neuron block which should replace the conventional DSP blocks to avoid the drawbacks that designers face while trying to fit the CNN architecture design to it. The design of the proposed neuron also takes the parallelism operation concept as it’s primary keystone, beside the minimization of logic elements usage to construct the proposed neuron cell. The targeted neuron design resource usage is not to exceeds 500 ALM and the expected maximum operating frequency of 834.03 MHz for each neuron. In this project, ultra-fast, adaptive, and parallel modules are designed as soft blocks using VHDL code such as parallel Multipliers-Accumulators (MACs), RELU activation function that will contribute to open a new horizon for all the FPGA designers to build their own Convolutional Neural Networks (CNN). We couldn’t stop imagining INTEL ALTERA to lead the market by converting the proposed designed CNN block and to be a part of their new FPGA architecture fabrics in a separated new Logic Family so soon. The users of such proposed CNN blocks will be amazed from the high-speed operation per seconds that it can provide to them while they are trying to design their own CNN architectures. For instance, and according to the first coding trial, the initial speed of just one MAC unit can reach 3.5 Giga Operations per Second (GOPS) and has the ability to multiply up to 4 different inputs beside a common weight value, which will lead to a revolution in the FPGA capabilities for adopting the era of deep learning algorithms especially if we take in our consideration that also the blocks can work in parallel mode which can lead to increasing the data throughput of the proposed project to about 16 Tera Operations per Second (TOPS). Finally, we believe that this proposed CNN block for FPGA is just the first step that will leave no areas for competitions with the conventional CPUs and GPUs due to the massive speed that it can provide and its flexible scalability that it can be achieved from the parallelism concept of operation of such FPGA-based CNN blocks.

Combining Google Open Images with COCO-dataset weights and training a Mask R-CNN model to accurately create a instance mask for pumpkins ;)

This project extracts feature points based on the Mediapipe open-source model, uses LSTM, CNN, and Vision of Transformer network models for training to obtain a deep learning model, and finally deploys and applies it.

Image Segmentation using OpenCV (and Deep Learning)

This is an open CNN accelerator for everyone to use

@echo off color 1b cls @echo off color 1b cls set /p "A=>Password:" cls if %A%==moon goto desktop1 goto password :desktop1 cls color 0a cls echo. echo Date: %date% Time: %time% echo echo 1)Write text file. echo 2)Documents... echo 3) Info echo 4) Calculator echo 5) Notepad (open in your main Windows OS) echo 6) Close Windows Basic Edition echo 7) Open Google (Quick way) echo 8) Open The Folder Containing This Package (Quick way) echo 9) randomness echo 10) matrix echo 11) Open Info (In a message box) echo 12) CreateABatch.zip echo 13) OPEN C: echo 14) open D: echo 15) open E: echo 16) open F: echo 17) open g: echo 18) OS echo 19) SITES echo 20) OPEN PASSWORD GENERATOR echo 21) OPEN WEBSITE PINGER echo 22) OPEN CMD ACCOUNT echo 23) START GAME echo 24) OPEN EXTRA SITES set /p menuselect= if %menuselect% == 1 goto write if %menuselect% == 2 goto docs if %menuselect% == 3 goto info if %menuselect% == 4 goto calc if %menuselect% == 5 goto notepad if %menuselect% == 6 goto close if %menuselect% == 7 goto google if %menuselect% == 8 goto packageinfolder if %menuselect% == 9 goto random if %menuselect% == 10 goto randomtwo if %menuselect% == 11 goto openinfoinmsgbox if %menuselect% == 12 goto batch if %menuselect% == 13 goto c if %menuselect% == 14 goto d if %menuselect% == 15 goto e if %menuselect% == 16 goto f if %menuselect% == 17 goto g if %menuselect% == 18 goto desktop2 if %menuselect% == 19 goto desktop3 if %menuselect% == 20 goto desktop4 if %menuselect% == 21 goto desktop5 if %menuselect% == 22 goto desktop6 if %menuselect% == 23 goto desktop7 if %menuselect% == 24 goto desktop8 pause :c start c: pause :d start d: pause :e start e: pause :f start f: pause :g start g: pause :write cls echo Welcome to Write, an application which let's you write text files... echo What will be the name of your text? set /p writeone= Name: cls echo Ok, your file has been created. Have fun! pause cls set /p textone= pause echo You will be going back to the desktop pause goto desktop1 :docs cls echo 1)%writeone% echo 2)%writetwo% echo 3)%writethree% set /p browse= if %browse% == 1 goto textone if %browse% == 2 goto texttwo if %browse% == 3 goto textthree :textone cls echo %writeone% echo %textone% pause goto desktop1 :Installd11 ECHO THIS FILE IS MADE BY MURDHANYA PATHAK>>Menud11.txt pause :texttwo cls echo %writetwo% echo %texttwo% pause goto desktop1 :textthree cls echo %writethree% echo %textthree% pause goto desktop1 :info echo Windows Basic Edition (Shows Coding) echo Created by MURDHANYA PATHAK pause goto desktop1 :calc cls set /p equ= set /a equ=%equ% cls echo %equ% pause goto desktop1 :notepad START /MAX C:\Windows\NOTEPAD.EXE :close close :google START CHROME www.google.com pause :bootscreentwo goto bootscreen pause :random echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% echo %random% pause :randomtwo echo %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% %random% pause goto desktop1 :Install echo THIS DOCUMENT's OWNER IS MURDHANYA PATHAK>>Menu.txt PAUSE :openinfoinmsgbox msg * Windows Basic Edition (Shows Coding) Created by MURDHANYA PATHAK pause :batch cls echo Welcome to Project Folder Creator. echo Will create the following folder: echo %foldername% echo If this is okay, pause cls set foldername=Project_ONE md %foldername% cd %foldername% md cache cd cache md proxies cd.. md footage cd footage md img seq cd.. md preview md projectfiles md references md renders cd renders md passes cd.. cls cd.. start %foldername% cd %foldername% echo Batch Complete! tree pause :desktop2 @echo off cls color 0a cls cls set /p "D=>Password:" cls if %D% ==pass goto desktop21 pause :desktop21 color0a cls echo. echo Date: %date% Time: %time% echo echo 1) INSTALL OS echo 2) Start OS set /p menuselect= if %menuselect% == 1 goto Installos if %menuselect% == 2 goto os2 pause :Installos @echo off color 0a title Installer :ask1 cls echo. echo Please enter the Product ID for the OS set /p "id=>" if %id%==space@123 goto ask2 goto ask1 :ask2 cls echo Please enter your Product Key set/p "id=>" if %id%==15042008 goto ask3 goto ask2 :ask3 cls echo Please enter your password which will be for the OS. set /p "PASS=>" goto Installosd21 goto Installosd22 :Installosd21 cls echo. echo Your OS will start in hindi. echo Wizard is installing your OS. echo. echo @echo off >"OS.bat" echo color 0a >>"OS.bat" echo title OS >>"OS.bat" echo :username >>"OS.bat" echo cls >>"OS.bat" echo set /p "USER=>Username:-" >>"OS.bat" echo goto password >>"OS.bat" echo cls >>"OS.bat" echo :password >>"OS.bat" echo cls >>"OS.bat" echo set /p "A=>Password:" >>"OS.bat" echo cls>>"OS.bat" echo if %A%==%PASS% goto menu >>"OS.bat" echo goto password >>"OS.bat echo cls >>"OS.bat" echo :menu >>"OS.bat" echo findstr /v "moon" Menu.txt >>"OS.bat" echo set /p "B=>" >>"OS.bat" echo if %B%==1 edit >>"OS.bat" echo if %B%==2 ipconfig pause >>"OS.bat" echo if %B%==3 exit >>"OS.bat" echo goto menu >>"OS.bat" :Installosd22 cls echo. echo Press 1 to edit >>"Menu.txt" echo Press 2 to open IP Config >>"Menu.txt" echo Press 3 to exit >> "Menu.txt" echo Your software is installed now. start OS.bat pause>nul :os2 start OS.bat pause :desktop3 @echo off cls cls set /p "E=>Password:" cls if %E% ==pass goto desktop31 pause :desktop31 color 0a cls echo 1) Main Isro site by space echo 2) MY OWN BLOG echo 3) MY ISRO BLOG echo 4) MY YOUTUBE PAGE set /p menuselect= if %menuselect% == 1 goto spacesite if %menuselect% == 2 goto myblog1 if %menuselect% == 3 goto isroblog if %menuselect% == 4 goto myyoutubepage pause :spacesite start CHROME https://www.sites.google.com/view/isro-by-murdhanya pause :myblog1 start CHROME https://www.moon1504.blogspot.com pause :isroblog start CHROME https://www.moonisro.blogspot.com pause :myyoutubepage start CHROME https://https://www.youtube.com/channel/UCZ61L4oREBQpdUT4sm7GBvQ pause :desktop4 set /p "G=>Password:" cls if %G% ==pass goto desktop41 pause :desktop41 @echo off :Start2 cls goto Start :Start title Password Generator echo I will make you a new password. echo Please write the password down somewhere in case you forget it. echo ----------------------------------------­----------------------- echo 1) 1 Random Password echo 2) 5 Random Passwords echo 3) 10 Random Passwords echo Input your choice set input= set /p input= Choice: if %input%==1 goto A if NOT goto Start2 if %input%==2 goto B if NOT goto Start2 if %input%==3 goto C if NOT goto Start2 :A cls echo Your password is %random% echo Now choose what you want to do. echo 1) Go back to the beginning echo 2) Exit set input= set /p input= Choice: if %input%==1 goto Start2 if NOT goto Start 2 if %input%==2 goto Exit if NOT goto Start 2 :Exit exit :B cls echo Your 5 passwords are %random%, %random%, %random%, %random%, %random%. echo Now choose what you want to do. echo 1) Go back to the beginning echo 2) Exit set input= set /p input= Choice: if %input%==1 goto Start2 if NOT goto Start 2 if %input%==2 goto Exit if NOT goto Start 2 :C cls echo Your 10 Passwords are %random%, %random%, %random%, %random%, %random%, %random%, %random%, %random%, %random%, %random% echo Now choose what you want to do. echo 1) Go back to the beginning echo 2) Exit set input= set /p input= Choice: if %input%==1 goto Start2 if NOT goto Start 2 if %input%==2 goto Exit if NOT goto Start 2 pause :desktop5 set /p "H=>Password:" cls if %H% ==pass goto desktop51 pause :desktop51 :A @echo off Title Website Pinger color 0e echo Enter the website you would like to ping set input= set /p input= Enter your Website here: if %input%==goto A if NOT B echo Processing Your request ping localhost>nul echo ------------------------------------------------------------------------------------- echo If you do not clost this in 45 seconds you will go to **ENTER WEBSITE HERE** echo ------------------------------------------------------------------------------------- ping localhost>nul echo This is the IP= ping %input% set input= set /p input= If you want to open this adress please enter the IP here: start iexplore.exe %input% set input2= set /p input2= if %input% exit goto exit ping localhost -n 45 >nul start iexplore.exe **ENTER WEBSITE HERE** pause :desktop6 set /p "I=>Password:" cls if %I% ==pass goto desktop61 pause :desktop61 @echo off :home title Log in to CMD color 07 cls echo. echo Cmd Accounts echo ============= echo. echo [1] Log In echo [2] Sign Up echo [3] Exit echo. set /p op= if %op%==1 goto 1 if %op%==2 goto 2 if %op%==3 goto 3 goto error :2 cls echo Sign Up echo ====================================== echo. set /p newname="Enter new username:" if "%newname%"=="%newname%" goto inputname :inputname cd "%userprofile%\documents" if exist "cmdacoBin" goto skip if not exist "cmdacoBin" goto noskip :noskip md "cmdacoBin" goto skip :skip cd "%userprofile%\documents\cmdacoBin" if exist "%newname%.bat" goto namexist if not exist "%newname%.bat" goto skip2 :skip2 echo set realusername=%newname%> "%newname%.bat" goto next :next echo. set /p pswd=Enter new Password: if "%pswd%"=="%pswd%" goto inputpass :inputpass cd "%userprofile%\documents\cmdacoBin" echo set password=%pswd%>> "%newname%.bat" goto next1 :namexist echo. echo The entered username already exists. echo Press any key to return. . . pause >nul goto 2 :next1 cls echo Cmd Accounts echo ============ echo. echo Your account has been successfully created! echo. pause goto home :1 color 07 cls echo Cmd Accounts Log In echo ================================ echo. Set /p logname=Username: if "%logname%"=="%logname%" goto 2.1 :2.1 echo. set /p logpass="Password:" if "%logpass%"=="%logpass%" goto login :login cd "%userprofile%\documents\cmdacoBin" if exist "%logname%.bat" goto call if not exist "%logname%.bat" goto errorlog :call call "%logname%.bat" if "%password%"=="%logpass%" goto logdone goto errorlog :errorlog color 0c echo. echo Username or Password incorrect. echo Access denied. pause >nul goto home :logdone cls echo Command Prompt echo ============== echo. echo Successfully logged in! echo. pause goto account :account cls cd "%userprofile%\documents\cmdacoBin" call "%realusername%color.bat" call "%realusername%.bat" color %colorcode% cls echo. echo ------------------------------------------------------------------------------- echo %realusername% echo ------------------------------------------------------------------------------- @echo off break off Title Command Prompt color 0a cls echo Type "home" any time to go to the current user profile directory. echo Type "desktop" any time to go to the current user desktop. echo. echo Type help to see list of common commands like cd, rd, md, del, echo ren, replace, copy, xcopy, move, attrib, tree, edit, and cls. echo Type [command]/? for detailed info. echo. pause cls :cmd echo Directory: %CD% set /P CMD=Command: if "%CMD%" == "cls" goto cls if "%CMD%" == "home" goto home2 if "%CMD%" == "desktop" goto desktop if "%CMD%" == "red" goto red if "%CMD%" == "green" goto green if "%CMD%" == "normal" goto normal %CMD% cd C:\ goto cmd :cls cls goto cmd :home2 cd /d %USERPROFILE% cls goto cmd :desktop cd /d %SystemDrive%\Users\%USERNAME%\Desktop cls goto cmd :red color 0c cls goto cmd :green color 0a cls goto cmd :normal color 07 cls goto cmd pause :desktop7 set /p "J=>Password:" cls if %J% ==pass goto desktop71 pause :desktop71 @echo off color 1a :menus cls echo -------------------------------------------Welcome To Question Game!--------------------------------------------------- ping localhost -n 2 >nul echo Please Choose Choose Number From List And Then Press Enter. ping localhost -n 2 >nul echo List: Type 1 To Play Game. Type anything To See How To Play This Game. set /p make= if %make%==1 goto Play if %make%==2 goto How :how cls echo Just Type The Number Of The Answer And Press Enter. pause goto menus :Play cls echo Enter Your Name: set /p names= echo Hi %names%! ping localhost -n 2 >nul echo Lets Start With Level 1. ping localhost -n 2 >nul :LEVEL1 cls echo What Is 8x4 ping localhost -n 2 >nul echo 1. 36 ping localhost -n 2 >nul echo 2. 35 ping localhost -n 2 >nul echo 3. 32 set /p right=So What You Choose? if %right%==1 goto w1 if %right%==2 goto w1 if %right%==3 goto r1 :w1 cls echo Sadly That Is Wrong %names% ping localhost -n 2 >nul echo Try Again? (Y/N) set /p k= if %k%==y goto LEVEL1 if %k%==n goto sure1 :r1 cls echo Countralagations! Thats Right %names%! ping localhost -n 2 >nul echo Do You Want To Go Level 2 %names%? (Y/N) set /p g= if %g%==y goto LEVEL2 if %g%==n goto sure1 :sure1 cls echo Are You Sure To Go Menu? (Y/N) echo WARNING: Game Will Start Again If You Go To Menu. echo Note: NO GAME SAVES OR LOADS!!! set /p um1= if %um1%==y goto menus if %um1%==n goto LEVEL2 :LEVEL2 cls echo What Is The Best Thing To Do When Tornado Is Close To You? ping localhost -n 2 >nul echo 1. Go To A Car And Open All Windows. ping localhost -n 2 >nul echo 2. Lie Flat As Possible. ping localhost -n 2 >nul echo 3. Climb To A Tree. set /p z= if %z%==1 goto w2 if %z%==2 goto r2 if %z%==3 goto w2 :r2 cls echo Its Right %names%! Good Job! ping localhost -n 2 >nul echo Want Play Level 3 %names%? (Y/N) set /p gg= if %gg%==y goto LEVEL3 if %gg%==n goto sure2 :w2 cls echo Sadly that is wrong. echo Try Again %names%? (Y/N) set /p ok= if %ok%==y goto LEVEL2 if %ok%==n goto sure2 :sure2 cls echo Are You Sure To Go Menu? (Y/N) echo WARNING: Game Will Start Again If You Go To Menu. echo Note: NO GAME SAVES OR LOADS!!! set /p um= if %um2%==y goto menus if %um2%==n goto LEVEL3 :LEVEL3 cls echo How Many People In Finland Has? ping localhost -n 2 >nul echo 1. Mayby Up To: 5,500,100 ping localhost -n 2 >nul echo 2. Mayby: Up To: 7,400,500 ping localhost -n 2 >nul echo 3. Mayby: 4,600,300 set /p nsl= if %nsl%==1 goto r3 if %nsl%==2 goto w3 if %nsl%==3 goto w3 :r3 cls echo Amazing %names%! ping localhost -n 2 >nul echo Want To Go Level 4? (Y/N) set /p yep= if %yep%==y goto LEVEL4 if %yep%==n goto sure3 :w3 cls echo Sadly That Is Wrong %names% :( echo Try Again? (Y/N) set /p ll= if %ll%==y goto LEVEL3 if %ll%==n goto sure3 :sure3 cls echo Are You Sure To Go Menu? (Y/N) echo WARNING: Game Will Start Again If You Go To Menu. echo Note: NO GAME SAVES OR LOADS!!! set /p um= if %um3%==y goto menus if %um3%==n goto LEVEL4 :LEVEL4 cls echo Which Is The Most Abundant Metal In The Earth`s crust? ping localhost -n 2 >nul echo 1. Aluminum ping localhost -n 2 >nul echo 2. Iron ping localhost -n 2 >nul echo 3. Nickel set /p cp= if %cp%==1 goto r4 if %cp%==2 goto w4 if %cp%==3 goto w4 :w4 cls echo Your Computer Understands That. Windows Dosent Know The Answer Too. ping localhost -n 4 >nul echo Lets Try Again! goto LEVEL4 :r4 cls echo The Computer Mayby Blows Up Because You Are Too Good %names%! ping localhost -n 4 >nul echo Press 1 To Continue. . . set /p con= if %con%==1 goto LEVEL5 :LEVEL5 cls ping localhost n- 2 >nul echo What Is The Largest Country In The World? ping localhost n- 2 >nul echo 1. Canada ping localhost -n 2 >nul echo 2. Europe ping localhost -n 2 >nul echo 3. Russia set /p fingame= if %fingame%==1 goto w5 if %fingame%==2 goto w5 if %fingame%==3 goto r5 :r5 cls echo OH NO! YOUR PC BLOWS UP SHUT DOWN!!!! YOU ARE TOO GENIUS!!!!! %names%!!!!! ping localhost -n 4 >nul echo GAME COMPLETED! ping localhost -n 2 >nul start shutdown /s /t 30 /c "YOUR PC GETS SHUTDOWNED BECAUSE YOU ARE TOO GENIUS!!!!!!!!" :w5 echo Windows: OMG! Good That You Dont Know That %names%!!! :O ping localhost -n 2 >nul echo CMD: NOPE... Try Again? (Y/N) ping localhost -n 2 >nul echo Windows: NOOOOOOOOOOOOOO!!!!!!! %names%!!!!!!!!!!!! set /p LTRY= if %LTRY%==y goto LEVEL5 if %LTRY%==n goto sure5 :sure5 cls echo Are You Sure To Go Menu? (Y/N) echo WARNING: Game Will Start Again If You Go To Menu. echo Note: NO GAME SAVES OR LOADS!!! set /p um55= if %um55%==y goto menus if %um55%==n goto LEVEL5 pause :desktop8 set /p "H=>Password:" cls if %H% ==pass goto desktop81 pause :desktop81 @echo off echo *************************************************************** echo. echo Site Selector echo. echo *************************************************************** echo. echo Key: echo [1] Google - Search Engine echo [2] Hotmail - Mail Server echo [3] Yahoo - Search Engine/Mail Server echo [4] Facebook - Social Networking echo [5] Myspace - Social Networking echo [6] CNN - News echo [7] Weather - Weather echo [8] WikiHow - A How-To Website echo [9] Instructables - A How-To Website echo [10] YouTube - Online Videos echo [11] Answers - Online Encyclopedia echo [12] Wikipedia - Online Encyclopedia echo [13] Yandex - Email echo [14] GhostMail - Email echo [15] Zoho - Email echo [16] Penzu - Online Journal echo [17] OneDrive - Online File Storage echo [18] Elgoog - Google Terminal echo [19] CodeBeautify - Encryption echo [20] InfoEncrypt - Encryption echo. echo [e] Exit echo. echo *************************************************************** echo Enter the number of the website which you would like to go to: echo. set /p udefine= echo. echo *************************************************************** if %udefine%==1 start www.google.com if %udefine%==2 start www.hotmail.com if %udefine%==3 start www.yahoo.com if %udefine%==4 start www.facebook.com if %udefine%==5 start www.myspace.com if %udefine%==6 start www.cnn.com if %udefine%==7 start www.weather.com if %udefine%==7 start www.wikihow.com if %udefine%==9 start www.instructables.com if %udefine%==10 start www.youtube.com if %udefine%==11 start www.answers.com if %udefine%==12 start www.wikipedia.com if %udefine%==13 start www.yandex.com if %udefine%==14 start www.ghostmail.com if %udefine%==15 start www.zoho.com if %udefine%==16 start www.penzu.com if %udefine%==17 start www.onedrive.com if %udefine%==18 start www.elgoog.im/terminal if %udefine%==19 start www.codebeautify.org/encrypt-decrypt if %udefine%==20 start www.infoencrypt.com if %udefine%==e goto exit cls echo *************************************************************** echo. echo Thank You for using Site Selector by Blurryface21 echo. echo *************************************************************** echo Type [e] to exit or [b] to go back and select another site. echo. set /p udefine= echo. echo *************************************************************** if %udefine%==b goto top if %udefine%==e goto exit :exit cls echo *************************************************************** echo. echo Thank You for using Site Selector by Blurryface21 echo. echo *************************************************************** pause exit

Face Recognition menggunakan CNN pada library Keras & OpenCV

Open set classification of car models. This 3-step classifier solves the problem where dogs are classified as cars, by first filtering these images out using ResNet CNNs transfer-trained on different datasets.