Welcome to the official page of the Project module of the course on Introduction to Machine Learning taught to the 1st year Master's students of the Innopolis University. The project will be run in the form of a competition. Details are as follows.
The problem that you will solve here is object recognition for 9 objects: {airplane, car, bird, cat, deer, dog, horse, ship, truck}.
You will build a model that takes as input a batch of RGB images of objects with shape (32, 32, 3). Note that the test sets will be of shape (n_samples, 32, 32, 3).
In this competition, there will be two domains to which the data belong. This means there are two different probability distributions from which the images were sampled. We will denote them by P_{s}(x, y) and P_{t}(x, y). Look at the Figures below to preview samples from both domains:
Images from Domain P_{s}(x, y):
Images from Domain P_{t}(x, y):
We are interested in the generalization of your model, so your model should perform well on both domains over the unseen/hidden datasets. For that purpose we have divided our dataset into 4 sets:
There's an option, not to use the unlabeled dataset, however, your score may suffer. It is your job to figure out a way to use the given unlabeled dataset (Xt) to adapt your model to the other domain. We will support you with related topics and some references that may guide you.
The competition is divided into two phases:
So, the first phase is for you to tune your model, and the second phase for us to evaluate it.
The final grade will be a combination of your score and your rank:
Let your score on the private test set be a ∈ [0, 1] and your rank is r ∈ [1, S], while S is the number of participants or students.
So, there are 50 students and:
Cheating is a serious academic offense and will be strictly treated for all parties involved. So delivering nothing is always better than delivering a copy.
from keras import ...
", instead USE "from tensorflow.keras import ...
".model.predict(x)
", while x is of shape (n_samples, 32, 32, 3).xs = np.load('xs.npy')
".To Submit:
model.save('model.h5')
", Take care, it should have the name 'model.h5'.We will list several topics and a few references associated with it. You are free to use these references or any others as long as you won't violate the competition rules.
1- Unsupervised deep learning:
2- Transfer Learning and Domain Adaptation
Start: Nov. 15, 2020, midnight
Start: Dec. 14, 2020, midnight
Dec. 14, 2020, 11 p.m.
You must be logged in to participate in competitions.
Sign In# | Username | Score |
---|---|---|
1 | dfokin | 0.7813 |
2 | YaroslavPlaskin | 0.7490 |
3 | Mostafa_Hegazy | 0.7271 |