Banana Image Recognition Using Deep Learning

TASK 1 Create an object detection model using R-CNN, Yolo, or SSD. Since there's no small dataset such as MNIST and Fashion-MNIST in object detection, you can use the dataset from the d2l/notebooks/mxnet/chapter_computer-vision/[login to view URL] notebook. The dataset has 1000 banana images with different rotations and sizes placed randomly on some background images. 1.A Obtain the dataset. Working on a small dataset such as the one from the [login to view URL] would allow you to train and test different models without taking hours/days to finish. Go to the notebook and obtain the dataset. 1.B Build an object detector. • using YOLO, and SSD. (need two separate notebooks) • Using the dataset, train, or fine-tune (a pre-trained model) to create an object detector. • Test on the test split of the dataset and report the results. • Show image examples of the detection performance. • Note, all platforms, i.e., mxnet, pytorch, tensorflow (and keras), have pre-trained models. You can use them, but you'll need to fine-tune and show your edits to obtain your own results. TASK 2 • Machine translation and seq2seq modeling • Build a seq2seq model for machine translation (English to French) • Using the example in [login to view URL], build your own model. • The notebook shows how to load the dataset. • Check the [login to view URL] for more info. • Use LSTM or GRU, With more than 2 layers and more than 128 hidden units. • Report the loss after the training. • Use the model to predict multiple sentences. • Extra (optional): Use BLEU to evaluate the predicted translations.