Blood Cell Detection Using Naïve ApproachLast Updated on May 3, 2021
Object detection problem applied for detecting blood cells in microscopic images. Used VGG16 'imagenet' weights for preprocessing data and passed into a dense layer to obtain predictions.
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Real Time Object Detection Using TensorflowLast Updated on May 3, 2021
Object detection is a computer vision technique in which a software system can detect, locate, and trace the object from a given image or video. The special attribute about object detection is that it identifies the class of object (person, table, chair, etc.) and their location-specific coordinates in the given image. The location is pointed out by drawing a bounding box around the object. The bounding box may or may not accurately locate the position of the object. The ability to locate the object inside an image defines the performance of the algorithm used for detection. Face detection is one of the examples of object detection.
These object detection algorithms might be pre-trained or can be trained from scratch. In most use cases, we use pre-trained weights from pre-trained models and then fine-tune them as per our requirements and different use cases.
Generally, the object detection task is carried out in three steps:
- Generates the small segments in the input as shown in the image below. As you can see the large set of bounding boxes are spanning the full image
- Feature extraction is carried out for each segmented rectangular area to predict whether the rectangle contains a valid object.
- Overlapping boxes are combined into a single bounding rectangle (Non-Maximum Suppression)
Tensorflow is an open-source library for numerical computation and large-scale machine learning that ease Google Brain TensorFlow, the process of acquiring data, training models, serving predictions, and refining future results.
- Tensorflow bundles together Machine Learning and Deep Learning models and algorithms.
- It uses Python as a convenient front-end and runs it efficiently in optimized C++.
- Tensorflow allows developers to create a graph of computations to perform.
- Each node in the graph represents a mathematical operation and each connection represents data. Hence, instead of dealing with low-details like figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application.
The TensorFlow Object Detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models.
- There are already pre-trained models in their framework which are referred to as Model Zoo.
- It includes a collection of pre-trained models trained on various datasets such as the
- COCO (Common Objects in Context) dataset,
- the KITTI dataset,
- and the Open Images Dataset.
As you may see below there are various models available so what is different in these models. These various models have different architecture and thus provide different accuracies but there is a trade-off between speed of execution and the accuracy in placing bounding boxes.
Tensorflow allows developers to create a graph of computations to perform. Each node in the graph represents a mathematical operation and each connection represents data. Hence, instead of dealing with low-details like figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application.
The deep learning artificial intelligence research team at Google, Google Brain, in the year 2015 developed TensorFlow for Google’s internal use. This Open-Source Software library is used by the research team to perform several important tasks.
TensorFlow is at present the most popular software library. There are several real-world applications of deep learning that makes TensorFlow popular. Being an Open-Source library for deep learning and machine learning, TensorFlow finds a role to play in text-based applications, image recognition, voice search, and many more. DeepFace, Facebook’s image recognition system, uses TensorFlow for image recognition. It is used by Apple’s Siri for voice recognition. Every Google app that you use has made good use of TensorFlow to make your experience better.
Here mAP (mean average precision) is the product of precision and recall on detecting bounding boxes. It’s a good combined measure for how sensitive the network is to objects of interest and how well it avoids false alarms. The higher the mAP score, the more accurate the network is but that comes at the cost of execution speed which we want to avoid here.
As my PC is a low-end machine with not much processing power, I am using the model ssd_mobilenet_v1_coco which is trained on COCO dataset. This model has decent mAP score and less execution time. Also, the COCO is a dataset of 300k images of 90 most commonly found objects so the model can recognise 90 objects.
This brings us to the end of this project where we learned how to use Tensorflow object detection API to detect objects in images
Ai Based Attendance System Using Neural NetLast Updated on May 3, 2021
This repositoy contains two folders : Problem Statement Part 1 (For Hackathon Model) and Problem Statement Part 2 (For report on FCN in Autonomous Vehicle)
The model for Hackathon is trained using the idea that each folder in Trainset is a department for the comapny with image of each employee (both passport and selfie). This was necessary for now as training the model using each employee's picture was not possible due to availability of less documents.
The above code for task 1 of Deep Learning CV Hackathon is trained on the data set in the link https://drive.google.com/file/d/12_WTFi9ppvD-loaWUWpUar25Z3nT5k9P/view
Hackathon.ipynb is the trained model that was trained in GOOGLE COLAB, so I would recommend you to run the hackathon.ipynb on Google Colab, in case you don't want to use !mkdir or !unzip in your code, you can run the file in jupyter even, after commenting these statements.
hackathon.py is the required python file that takes in two arguments : the selfies image and passport image along with the extension and directory ( if not present in the same folder as the hackathon.py model). The file should be run on python IDE , not on Command Prompt as this has been programmed accordingly.
haarcasccade_frontalface_default.xml file has been used for Face Region of Interest detection. This needs to be in the same folder as Hackathon.ipynb and hackathon.py
The model is saved by the name of saved_model .
Machine Learning AlgorithmsLast Updated on May 3, 2021
I have created this projects by learning some machine learning algorithm's
Different algorithms I have learned are :
- K-means : In k-means algorithm I learned the working of algorithm using a dataset and loaded it. I have used sklearn and used metrics function in it to predict best fit score for dataset.
- KNN : In this algorithm I have used a car.data csv file in order to perform operations on it. I have trained the data and then labels of columns in order to fit the data in model. Then have predicted the acurracy of model.
- Regression : In this algorithm I have used different libraries such as numpy, pandas, sklearn and matplotlib for working on excel file. numpy and pandas is used for reading the csv file form directory and also to use series and dataframes of pandas. Matplotlib is used for graphical representation of model and sklearn is used to import its linear model for the data and train the data.
- SVM(Support Vector Machine Algorithm): In this algorithm I have used a different data set. Loaded that dataset into the algorithm with help of sklearn. works for classification and for regression, svm uses hyperplay to divide data in straights(line, 4D). Its a linear way to divide data.
Bank_Loan_Default_CaseLast Updated on May 3, 2021
The Objective of this problem is to predict whether a person is ‘Defaulted’ or ‘Not Defaulted’ on the basis of the given 8 predictor variables.
The data consists of 8 Independent Variables and 1 dependent variable. The Independent Variables are I. Age: It is a continuous variable. This feature depicts the age of the person. II. Ed: It is a categorical variable. This feature has the education category of the person converted to numerical form. III. Employ: It is a categorical variable. This feature contains information about the geographic location of the person. This column has also been converted to numeric values. IV. Income: It is a continuous variable. This feature contains the gross income of each person. V. DebtInc: It is a continuous variable. This feature tells us an individual’s debt to his or her gross income. VI. Creddebt: It is a continuous variable. This feature tells us about the debt-to-credit ratio. It is a measurement of how much a person owes their creditors as a percentage of its available credit. VII. Othdebt: It is a continuous variable. It tells about any other debt a person owes. VIII. Default: It is a categorical variable. It tells whether a person is a Default (1) or Not-Default (0).
After performing extensive exploratory data analysis the data is given to multiple models like Logistic Regression, Decision Tree classifier, Random Forest classifier, KNN, Gradient Boosting classifier with and without hyperparameter tuning, the final results are obtained and compared on metrics like precision score, recall score, AUC-ROC score.
E-CommerceLast Updated on May 3, 2021
- Implement E-Commerce Web App which had started from 13 November to 12 December 2020.
- In this Web App user can able to purchase the various products which is available in Database and virtually placing the orders.
- This Web App consist of proper Database functionality which help to implement different function and operations.
- User can able to ask any query regarding products and processes , also there is special search functionality in which user can able to filter their required products by simply search on there.
- There are pop-down Cart which shows the product available in the Cart which is select by the user with two buttons in the bottom, one is Checkout and another is Clear Cart.
- On clicking the Checkout button it render the user to the place order page in which user should give all their details by filling the blanks input and finally place the Order.
- All the orders detail of user, orders and query will be stored in the Databases with their Username and Date.
- On clicking the Clear Cart button , it clear all the product which is select by the user for purchase.