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# MNIST Classification
This is an example grader which implements a simple machine learning classification task.
## Contents
1. [Overview](#task)
2. [Deployment Instructions](./DEPLOY.md)
## Task
Here, we are evaluating one of most common image classification problems. Given images of handwritten digits(0-9), one needs to correctly identify the number on it. It is based on MNIST (Modified National Institute of Standards and Technology database) which is a large database of handwritten digits that is commonly used for training various image processing systems.
## Evaluator Structure
```
.
├── README.md
├── aicrowd.yaml
├── data
│ └── example_ground_truth.csv
├── evaluator.py
├── requirements.txt
└── sample-submissions
└── example_submission.csv
```
**NOTE**: You don't need to include the `.images` directory in your evaluator.
### aicrowd.yaml
```yaml
challenge:
name: mnist
template: broker
description: Sample MNIST classification evaluator
scoring:
evaluator_path: evaluator.py
ground_truth_path: data/example_ground_truth.csv
```
- `challenge`: It has basic information of the challenge and defines the template - `broker` which is used for similar evaluators.
- `scoring`: It describes the file - `evaluator.py` used for evaluating the submission. Any number of arguments being used by the evaluator class can be listed here.
### data
This folder can be used to store any kind of data file which is used during evaluation. In this case, the ground truth - `example_ground_truth.csv`.
### `evaluator.py`
This is the main evaluator which evaluates the submission. Here, the submission in read in pandas post which python's sklearn module is used to calculate f1 and `log_loss_score`. The values are returned as `score` and `score_secondary` respectively. This file can be easily customized according to your own evaluation methods and scoring scheme.
```python
f1 = f1_score(ground_truth, submission, average = 'micro')
log_loss_score = log_loss(ground_truth, y_pred, labels = labels)
_result_object = {
"score": f1,
"score_secondary": log_loss_score
}
```
For challenge, with the concept of public/private score, we use meta parameters.
```python
_result_object = {
"score": f1,
"score_secondary": log_loss_score,
"meta": {
"private_score": 0.0,
"private_score_secondary": 0.0
}
}
```
Here the numbers in score & score_secondary will be visible to participants during the challenge, while "private_*" are hidden for them .
Once the challenge is over, we can change it to "private_score" & "private_score_secondary" in Leaderboard settings.
### requirements.txt
This is the requirements file for `evaluator.py` and contains all the python modules which might be needed for evaluation.
## Scoring
The scoring in this challenge is done by `evaluator.py` which uses the ground truth as reference to compute the `f1` and `log_loss_scores`. The result object is then sent to the web application to display the scores on the leaderboard!
The setup is flexible and any method or technique can be used in the evaluator to come up with `score` and `score_secondary`. Multiple submission types are also supported which can be defined in the `aicrowd.yaml` as mentioned above.
# Deployment
[Deployment Instructions](./DEPLOY.md)
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## We have curated frequently asked questions and common mistakes on Discourse, you can read them here: [FAQ and Common mistakes](https://watching.nwsautodaily.com/zh/)
# 📎 Important links
💪 VER AHORA ☛☛
https://gitlab.aicrowd.com/aicrowd/loginpass/-/merge_requests/11
💪 ✅➤➤Sub tw zh ➫ ➫ https://gitlab.aicrowd.com/aicrowd/example-grader/-/merge_requests/12
💪 ✅➤➤Sub tw HK ➫ ➫https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/merge_requests/575
👉 [Challenge page](https://www.aicrowd.com/challenges/airborne-object-tracking-challenge?utm_source=starter-kit&utm_medium=click&utm_campaign=prime-air)
[![Discord](https://img.shields.io/discord/565639094860775436.svg)](https://discord.gg/hAuevqx9Tj)