Commit f5969f63 authored by ashivani's avatar ashivani
Browse files

Update README.md

parent 6205c3d3
......@@ -2,28 +2,32 @@
# 🕵️ Introduction
![](https://storage.googleapis.com/kaggle-competitions/kaggle/4104/media/retina.jpg)
![](https://imgur.com/H7U23Tu)
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world.Deep Learning has given us tremendous power in the field of computer vision. In some fields of vision ,computers can now see and perceive beyond human capabilities. But with great power comes responsibility. The problem we have for you is to `classify` the patient `retina` as being `diabetic` or `not diabetic` taking into consideration the available image features in the dataset. To know more about diabetic retinopathy click [here](https://www.aao.org/eye-health/diseases/what-is-diabetic-retinopathy).
Test your vision of ML by this vision problem of Diabetes. Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world.
Here is a problem for you to classify the patient retina as being diabetic or not diabetic taking into consideration the available features of dataset. To know more about diabetic retinopathy click [here](https://www.aao.org/eye-health/diseases/what-is-diabetic-retinopathy).
Understand with code! Here is [getting started code](https://discourse.aicrowd.com/t/baseline-mnist/2757) for you.😄
# 💾 Dataset
This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. There are total of `20` attributes to this dataset, out of which first `19` attributes represents a descriptive features extracted from the image set. Last attribute `label` is `1` if image contains signs of Diabetic Retinopathy and `0` if no signs of Diabetic Retinopathy.
For details about attributes visit [here!](https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_DIBRD_challenge/blob/master/dataset_info.txt).
This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. There are total of 20 attributes to this dataset, out of which first `19` attributes represents a `descriptive features` extracted from the image set. Last attribute label is `1` if image shows signs of `Diabetic Retinopathy` and `0` if image does `not` show signs of Diabetic Retinopathy. For details about attributes visit [here!](https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_DIBRD_challenge/blob/master/dataset_info.txt).
## 📁 Files
- `./data/train.csv` - (`920` samples) File that should be used for training and validation purpose by the user.
- `./data/test.csv` - (`230` samples) File that will be used for actual evaluation for the leaderboard score.
Following files can be found in `resources` section:
- `train.csv` - (`920` samples) File that should be used for training and purpose by the user. It contains in csv format, the feature representation of the images along with the binary label for each such represntation.
.
- `test.csv` - (`230` samples) File that will be used for actual evaluation for the leaderboard score. It contains only the feature representation of the images and not their binary labels.
# 🚀 Submission
- Prepare a csv containing header as `label` and predicted value as digit `0` or `1` with name as `submission.csv`.
- Sample submission format available at `./data/sample_submission.csv`.
- Prepare a csv containing header as label and predicted value as digit `0` or `1` representing whether or not the image shows signs of `diabetic retionpathy`.
- The name of above file should be `submission.csv`.
- Sample submission format available at `sample_submission.csv`.
**Make your first submission [here](https://www.aicrowd.com/challenges/dibrd-predict-diabetic-retinopathy/submissions/new) 🚀 !!**
......@@ -52,4 +56,3 @@ During evaluation [F1 score](https://scikit-learn.org/stable/modules/generated/s
Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary, hajdu.andras@inf.unideb.hu
- Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
- [Image source](https://www.kaggle.com/c/diabetic-retinopathy-detection)
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment