class DataLoaders(GetAttr): def _init_(self, *loaders): self.loaders = loaders def _getitem_(self, i): return self.loaders train,valid = add_props(lambda i,self: self)ħ. Create a Data loaders class which will be used to transform our dataset into training and validation sets. animals = 'grizzly bear', 'fox','wolf' path = 'gdrive/MyDrive/Ai_projects/animal classifier' p_path=Path(path) fns = get_image_files(path) fnsĦ. The last lines get all the images in each folder in our path (from the lines above). The second and third lines are to initialize the path/directory to our datasets, this should follow the format used to save the files on google drive. ![]() We would be creating a list of the animals that would be classified with the first line in the cell below, ensure the names are the same as the ones used to name the folders containing each dataset. In google Colab, make sure that you go to runtime -> change runtime type -> GPU.)ĥ. (I recommend using Google Colab for this project as it makes the GPU configuration far easier. To use Google Colab, you can google search ‘google Colab’ and sign in using your Google account. I would be working with the google Colab notebook. We would be using the fastai2 library for this project, so make sure that is the version you have loaded in your notebook. It can become very frustrating if you are not working with the required version. ![]() The first thing you should know about this library is that it has different versions therefore, some methods/functions may not work in some versions. The Fastai library is a deep learning library with lots of really cool functions, it can be used from the data acquisition stage through to data cleaning, model development, and evaluation. Hey guys, today we will be using the Fastai library to create an image classifier.
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