Reptile Museum

Motive

New_Recording_-_4_7_2023._2_48_28_PM.mp4

The purpose of this project is to create an app to help people learn more about any kind of reptile around the world. It is also kid friendly. Just input an image of a reptile (such as Crocodile, Lizards, Snake, Turtle) this app will show you all the information such as Name, Scientific Name,Type,  generally where the reptile Commonly found in, Color, Food Habit, Conservation Status, Habitat in a very basic way.

Machine Learning Method

My goal is for the user to apply an input as an image and receive 8 outputs. So this is the reason I have to choose the Multi-Target Image Classification method as the backbone. 

Why did I choose Machine Learning when I can use Normal Data Structure/ Code?

If I use Data Structure / Algorithm / Explicite code, This will need huge data for individual species. Also Image Analysis is much more complex without ML. This is the reason I chose Machine Learning for Image classification.

Data Collection

I collected my data with the help of google, ChatGPT.

1. Collected Reptile Type names around the world. Currently I choose Crocodile, Lizard, Snake, Turtle.
   
2. For the individual Type I have collected Name of all species Also their Scientific Name, conservation Status,Habitat,Found In, Color, Diet. Using Wikipedia,National Geographic website, link and ChatGPT I ensured, my data is Correct or not. All data I have collected manually. 108 Species information is  collected.
   
3. According to Name I collected images. Basically, I scrape images from Google Photos using Selenium. Almost 50 images collected for each Name.

Data Preprocessing and Image Augmentation

As you can see, my dataset is small, and Google Images only has a few images for specific species, I generated more images based on the few images collected 6539, and I use 8 types of Augmentation(Rotate,Brightness,Flip etc) to increase this dataset. After all of this, my current data size is 45767 . Dataset

Model Training

For the rest of the tasks like training, testing, and compression, I choose Fastai Pytorch to get better performance and time effect.      For training, I use 3 types of models, resnet50, resnet34, xresnet18deeper, and I save the best model as pkl for future use.    Also, I did some experiments using batch size. I used 3 different batch sizes to see if there was any tiny effect on the model's accuracy. 

Batch size = 16, which made the model training process very slow and low-accurate.
Batch size = 32, made the model training process faster with the highest accuracy.
Batch size = 64, it gave the fastest training process but was not as accurate as bs = 32.

Final Model Selection

As I used 3 types of models here is their best results..

Model	Accuracy	F1_score	Precision	Time
Resnet50	.9981	0.930426	0.970514	08:20
Resnet34	0.998095	0.927186	0.956563	07:31
xresnet18_deeper	0.977500	0.023208	0.068224	05:40

As resnet50 and resnet34 both reach 99% Accuracy and another score is way close but resnet34 is a little bit faster. So I chose resnet34 for further work.

Inside of Final Model Training/ Analysis

For analysis on what is going on during training The model, I use Weight and Bias and create a dashboard. Here it is.

Model size Compression

Model compression is very essential, because we as developers want to reduce or manage app size. In that case, I use  onnx for model size compression. It also makes faster the model.  

Deployment

HuggingFace is the platform where I deployed my project. You can see this from here.

Interface

As this app is educational app, I've decided to make it free for all. So I made a simple UI using Flask and rendered it on the render.com website. This website is free with conditions.

Here is my app link.

• The background image of this website is generated using Stable Diffusion. An AI that generates unseen images using "promt" The promt is:  

  night view green Amazon deep forest with reptile anime , realistic,Cartoon, mdjrny-v4 style, HQ

• Here, I also used Google Text To Speech AI for kids who cannot read but have a curious mind and want to know about Reptile. Audio generates in Real Time  

Problem That I've faced and How did I Overcome

 Of course, I've faced so many obstacles to building this website.

First of all, set up the Dataloader. There are a few resources that I found for Multi-Target Image classifier. I've experimented in a lot of ways and finally MultiCategoryBlock() with the parameter add_na = True, this is for Multi-Target classification, but I believe this will also work for regression.
Secondly, I messed up to choose the right metric(s), finally I used accuracy_multi. This metric works not only well for multi-label classification but also  Multi-Target classification. I also used F1_score and Precision for a better understanding of my model.

Conclusion

I have completed this project like I wanted. If you want to develop more just send me a pull request. Thanks.