Fast.ai Course v3 Lesson 1 Notes
Lesson 1 notes
(56:20) Advice on how to get the most out of the course: “Pick one project, do it really well, make it fantastic."
(1:06:19) Deep neural network architectures for image classification: ResNet-50 is good enough, take a look at the DAWNBench.
plot_top_losses() to have a check on the model and how good is the prediction, by telling the “wrongest” predictions.
(1:14:00) Also use
most_confused() to exam the “wrongest” content suggested by confusion matrix.
(1:15:53) To train the whole model instead of just fine tune the pre-trained model by adding several extra layers at the end, use
(1:21:58) The reason of why just finetuning the original model doesn’t work: Different layers of neural network represent different levels of kind of semantic complexity, and all the layers, for example, updating the representations of diagonal lines, gradients, and the representations of eyeballs, are all trained in the same speed (learning rate actually).
(1:23:00) To improve the accuracy of the model, try to take a look the learning rate plot first.
(1:28:26) Use smaller batch size if the training model doesn’t fit into GPU memory.
Notes on implementation
As I run the codes and projects of the fasi.ai courses on Google Colab, below are some notes about proper Jupyter notebook setup.
Put the below 2 code blocks at the very beginning of the notebook.
# Permit Colaboratory instance to read and write files to Google Drive from google.colab import drive drive.mount('/content/gdrive', force_remount=True) root_dir = "/content/gdrive/My Drive/" base_dir = root_dir + 'fastai-v3/'
# Update packages and the course repo !curl -s https://course.fast.ai/setup/colab | bash
When importing image data for training, data augmentation is necessary to enhance trained model to generalize better.
- The easiest way is to pass
- We should also remember to include parameter
sizein those *“from_"* methods if the sizes of images are not the same, to ensure that all the images are cropped or padded to the same size that they can be collated into batches.