Post Competition Round: Completed #educational Weight: 10.0

# SNAKE

Venomous Snake Classification

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Join us for the closing webinar where top participants will discuss their solutions!

🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️Introduction

Afraid of snakes?😳 Us too!😣 Snakebites are one of the world’s most neglected public health crises. Each year, they result in ~100,000 deaths and leave ~40,000 people disabled. Not only that, the poor and rural communities are the most affected by snakebites due to their limited access to antivenoms and hospitals.

Check this out 👉 https://www.who.int/news-room/fact-sheets/detail/snakebite-envenoming

What if there was a way to detect if a particular snake is venomous or not? And thus figure out if the snakebite is deadly or not? Solving this problem helps further an important Sustainable Development Goal(SDG 3.3 neglected tropical diseases), and we are proud to encourage efforts towards tackling it.

In this challenge we explore the use of computer vision in correctly classifying snakes as venomous / non venomous.

Understand with code! Here is `getting started code` for you.`😄`

## 💾 Dataset

The dataset provided contains images of various species of snake with each image of size `224*224` pixels. The train set and validation set has around `54,000` and `6000` images respectively grouped into the classes of `venomous` and `non venomous` while the test set contains around `15,000` images which needs to be predicted. The dataset is quite vast and has following variations:

• variation by age

• variation by geography

• medically important venomous snakes

• similar looking harmless snakes

## 📁 Files

Following files are available in the `resources`` section:

• `train.zip` : (`54,000`) The zip file contains `train` folder with two folder inside it with name as `venomous` and `non venomous`.These folder contains the images of respective classes.

• `val.zip` : (`6,000`) The zip file contains `val` folder with two folder inside it with name as `venomous` and `non venomous`.These folder contains the images of respective classes.

• `test.zip` : (`15,000`) The zip file contains `test` folder with images of the snakes to whose classes needs to be predicted.

• `sample_submission.csv` : Sample submission format for the challenge.

## 🚀 Submission

• Prepare a CSV containing two coloums with header `id` and `class`. The first column should contains the image id(without the '.jpg' extension) and the second as the class it belongs to `venomous` or `non_venomous`.
• Name of the above file should be `submission.csv`.
• Sample submission format available at `sample_submission.csv` in the resorces section.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score and Accuracy will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$