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vss:competition:1 [2023/07/10 17:27] andy_eduard.catruna |
vss:competition:1 [2024/07/09 20:41] (current) andy_eduard.catruna [Competition] |
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| ====== Competition ====== | ====== Competition ====== | ||
| - | <hidden> | ||
| - | ===== Introduction ===== | + | The competition is hosted on Kaggle at this **[[https://www.kaggle.com/t/ca9c55b30f5e0d7e5b21d84d857a4819 | link]]**. |
| - | Traditional image classification models heavily rely on accurately labeled data for training, but in real-world scenarios, acquiring large quantities of precisely labeled images can be costly and time-consuming. | + | The teams will be composed of 2 people. |
| - | Additionally, label noise or mislabeling can further complicate the training process, leading to inaccurate predictions and decreased model performance. | + | We provide you with a **[[https://www.kaggle.com/code/andreiniculae/starting-code | Starter Code]]** which demonstrates how to read the data, train a network and make a submission. You are encouraged to start your work from this notebook. |
| - | In this challenge, we provide you with a dataset that poses both obstacles: a significant portion of the training data remains unlabeled, and a subset of the labeled data contains mislabeled instances. | + | ===== Description ===== |
| - | Your task is to develop innovative machine learning algorithms and techniques to overcome these challenges and build a robust image classification model. | + | Traditional image classification models heavily rely on accurately labeled data for training, but in real-world scenarios, acquiring large quantities of labeled images can be costly and time-consuming. |
| - | To succeed in this competition, participants are encouraged to explore semi-supervised learning methods that leverage the vast unlabeled data to improve the model's performance. Developing strategies to mitigate the impact of mislabeled instances and enhance the model's ability to generalize effectively will be crucial. Creative ideas are encouraged. | + | Additionally, label noise or mislabeling can further complicate the training process, leading to decreased model performance. |
| - | ===== Task Description ===== | + | In this challenge, we provide you with a dataset that poses both obstacles: a significant portion of the training data remains unlabeled, and an unknown percentage of the labeled data contains mislabeled instances. |
| - | * Image classification with 30 distinct classes | + | Your task is to develop innovative deep learning algorithms and techniques to overcome these challenges and build a robust image classification model. |
| - | * You have 15,000 images for training | + | |
| - | * 80% of the training set is unlabeled | + | To succeed in this competition, participants are encouraged to explore semi-supervised learning methods that leverage the unlabeled data to improve the model's performance. Developing strategies to mitigate the impact of mislabeled instances and enhance the model's ability to generalize effectively will be crucial. We encourage creative ideas. |
| - | * an unknown percentage of the labeled images are mislabeled | + | |
| + | ===== Data ===== | ||
| + | |||
| + | * 30 distinct classes. | ||
| + | * 15,000 images for training. | ||
| + | * 80% of the training set is unlabeled. | ||
| + | * an unknown percentage of samples are mislabeled. | ||
| + | |||
| + | ===== Deadline ===== | ||
| + | You can make submissions until 3:55 PM Friday (12 July). | ||
| ===== Rules ===== | ===== Rules ===== | ||
| - | * Using models pretrained on ImageNet is not allowed | + | <note important> |
| - | * | + | * Using pretrained models is not allowed. |
| + | * Using additional training data apart from the data provided is not allowed. | ||
| + | * Searching on the internet for the clean dataset or labels for the test set is not allowed. | ||
| + | </note> | ||
| - | The competition is hosted on Kaggle at this **[[https://www.kaggle.com/t/aa0298d7b1b3452baed400707a089924 | link]]** | + | <note important> |
| + | Slides | ||
| + | * Max 2 slides | ||
| + | * Use Google Slides | ||
| + | * Submit the link to your slides **[[https://forms.gle/gorF3d8GJLFkLhAs9|here]]** | ||
| + | </note> | ||
| - | </hidden> | ||
