Animal-Recognition-Image-Processing

This web app recognizes animals from their images using machine learning.

• Created a tool that recognizes an animal(>80% precision) when a user imports an image into it.

• This aids wildlife industries in recognizing animals for a variety of purposes (research, studies, etc.).

• Over 1500 animal images were used to create the model(may be updated).

• It takes an image as input and changes it to grayscale, no matter what size it is. It then does a hogs transformation and gives probabilities of the animal in the picture.

• Optimized SVM, KNN, SGD, and logistic regression, even checking learning curves to find the best model.

• Using Flask, I created a client-facing API.

• Add an image. Get the name of the animal in it. Try it!!!

Resources and Code

Python Version: 3.10.5

Packages: pandas, numpy, scikit-learn, scikit-image, scipy, flask, pickle, matplotlib, os, re, glob

For Web Framework Requirements: pip install -r requirements.txt

Heroku Productionization: https://animal-recognition-ai-app-c8135389da87.herokuapp.com/

Animal Types

The following animals were in the data:

• 'bear', 'cat', 'chicken', 'cow',

• 'deer', 'dog', 'duck', 'eagle',

• 'elephant', 'human', 'lion', 'monkey',

• 'mouse', 'natural', 'panda', 'pigeon',

• 'rabbit', 'sheep', 'tiger', 'wolf'.

Finding the data | Transforming it

I created paths towards the different image files and got them in an array.

Then, I applied the following transformations on the image array:

• Converted it grayscale

• Appropriately resized and scaled

• I used a hog transformer to convert it into a format that the model could understand.

• Appropriately resized and scaled

• Standardized the array

• Fit into the model

Model Building

I tried four different models and evaluated them using cohen kappa score. I chose cohen kappa score because it is relatively easy to interpret and particularly suited for image recognition scores.

In addition, to ensure that the models were not overfitting, I plotted their learning curves for test and training data and immediately identified which ones were poor.

Why did I choose these four different models:

• Stochastic gradient descent – Since the data is unconstrained.

• Logistic Regression – I thought there it could detect a logical pattern in an animal's shape after being hog transformed for example.

• KNeighborsClassifier – Again, since it groups neighbors, it could be suitable for grouping the hog transformed area and finding patterns.

• Support Vector Machine – Since there may be outliers(like different animals having the same shape), I thought SVM could capture that.

Model performance

The SVM model outperformed the other approaches on the test and validation sets and did not overfit.

• Support Vector Machine : CKS = 82.05%

• Logistic Regression: CKS = 77.43%

• KNeighborsClassifier: CKS = 62.61%

• Stochastic gradient descent: CKS = 56.4%

CKS = Cohen Kappa Score. >50% is usually good.

Productionization

In this step, I built a Flask API endpoint that was hosted on Heroku. The API webpage takes a photo of an animal and outputs the name of the animal that is in the photo.

https://animal-recognition-ai-app-c8135389da87.herokuapp.com/