Fast.ai Image Classification
We're going to use udt-dataset-cats-and-dogs, a dataset of labeled images of cats and dogs created from COCO. For this guide you don't need to download it directly, because we'll load it in right from our notebook.
Don't like cats and dogs? You can also use any classification from the Common Objects in Context with the Import COCO button! Maybe try to classify bears vs cats!
We can begin by importing the fastai library, pandas, and our udt.csv file.
from fastai.vision import *import pandas as pd​url_to_csv = "https://raw.githubusercontent.com/UniversalDataTool/udt-dataset-cats-and-dogs/master/coco_dogs_and_cats.udt.csv"udt_csv = pd.read_csv(url_to_csv)
You can use the udt.json format too, tables are just a nice way to visualize the data!
UDT Datasets just have links to images, so we'll need to download the actual images. Let's do that using the fast.ai download_images function.
# Get the lines of our CSV that have sample datasamples = udt_csv[udt_csv["path"].str.contains("samples.")]​# Create two csvs that just have# our cat image urls and dog image curls​dog_samples = samples[samples["annotation"] == "dog"]cat_samples = samples[samples["annotation"] == "cat"]​dog_samples.to_csv("dog_urls.csv", columns=["imageUrl"], header=False, index=False)cat_samples.to_csv("cat_urls.csv", columns=["imageUrl"], header=False, index=False)
# Now we can download the images!download_images("dog_urls.csv", "images/dog" , max_pics=500)download_images("cat_urls.csv", "images/cat" , max_pics=500)​# Let's make sure all the images are readableverify_images("images/dog", delete=True, max_size=500)verify_images("images/cat", delete=True, max_size=500)
From here, everything should should seem pretty normal. We can create an ImageDataBunch from our images directory.
data = ImageDataBunch.from_folder("images", train=".", valid_pct=0.2, ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)​# Let's take a look at the datadata.show_batch(rows=3, figsize=(7,8))
We can now train a model! This is just a simple one, don't forget to fine tune!
learn = cnn_learner(data, models.resnet34, metrics=error_rate)learn.fit_one_cycle(4)
