From c456a50a3bf02527ccc75293d3d2199f6aba7630 Mon Sep 17 00:00:00 2001 From: Oumaima Fisaoui <48260689+Oumaimafisaoui@users.noreply.github.com> Date: Sun, 6 Oct 2024 20:50:15 +0100 Subject: [PATCH] Chore(AI): Clarifying instructions --- subjects/ai/emotions-detector/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/subjects/ai/emotions-detector/README.md b/subjects/ai/emotions-detector/README.md index f95b70dee..670e0de08 100644 --- a/subjects/ai/emotions-detector/README.md +++ b/subjects/ai/emotions-detector/README.md @@ -67,7 +67,9 @@ Your goal is to implement a program that takes as input a video stream that cont This dataset was provided for this past [Kaggle challenge](https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/overview). It is possible to find more information about on the challenge page. Train a CNN on the dataset `train.csv`. Here is an [example of architecture](https://www.quora.com/What-is-the-VGG-neural-network) you can implement. **The CNN has to perform more than 60% on the test set**. You can use the `test_with_emotions.csv` file for this. You will see that the CNNs take a lot of time to train. - You don't want to overfit the neural network. I strongly suggest to use early stopping, callbacks and to monitor the training using the `TensorBoard` 'note: Integrating TensorBoard is not optional'. + You don't want to overfit the neural network. I strongly suggest to use early stopping, callbacks and to monitor the training using the `TensorBoard`. + + > Note: Integrating TensorBoard is mandatory. You have to save the trained model in `final_emotion_model.keras` and to explain the chosen architecture in `final_emotion_model_arch.txt`. Use `model.summary())` to print the architecture. It is also expected that you explain the iterations and how you end up choosing your final architecture. Save a screenshot of the `TensorBoard` while the model's training in `tensorboard.png` and save a plot with the learning curves showing the model training and stopping BEFORE the model starts overfitting in `learning_curves.png`.