Mon premier modèle d’apprentissage automatique

AI
Machine Learning
Python
Fastai
Author

Jason Suárez

Published

January 12, 2024

Goat Detector Training

This notebook utilizes the fastai and fastbook libraries to train a machine learning model for image classification. The goal is to classify images as either featuring Messi or Cristiano Ronaldo, two renowned football players. ## Importing Necessary Libraries Import fastai and fastbook libraries essential for image loading, preparation, and machine learning model operations.

%pip install -Uqq fastai fastbook Pillow nbdev
import fastbook
fastbook.setup_book()
from fastbook import *
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Figure 1

Loading and Preparing Images

Load the dataset containing images of Messi and Cristiano Ronaldo. This step includes preprocessing the images to make them suitable for training the model.

from fastcore.all import *
from fastai.vision.all import *
from PIL import Image

def search_images(term, max_images=30):
    "term : (string )name of the search target"
    print(f"Searching for '{term}'")
    return search_images_ddg(term, max_images)

urls = search_images('Cristiano Ronaldo photos', max_images=1)
urls[0]
Searching for 'Cristiano Ronaldo photos'
'http://www.fotolip.com/wp-content/uploads/2016/05/Cristiano-Ronaldo-1.jpg'
from fastdownload import download_url
dest = 'cristiano.jpg'
download_url(urls[0], dest, show_progress=False)

im = Image.open(dest)
im.to_thumb(256,256)

messi_dest = 'messi.jpg'
messi_image_url = search_images('Lionel Messi photos', max_images=1)
messi_image_url
Searching for 'Lionel Messi photos'
(#1) ['https://wallpapercave.com/wp/wp2043344.jpg']
download_url(messi_image_url[0], messi_dest, show_progress=False)
im_messi = Image.open(messi_dest)
im_messi.to_thumb(256,256)

searches = 'Cristiano Ronaldo','Lionel Messi'

path = Path('images')
from time import sleep

for o in searches:
    dest = (path/o)
    print(dest)
    dest.mkdir(exist_ok=True, parents=True)
    download_images(dest, urls=search_images(f'{o} photo'))
    sleep(10)  # Pause between searches to avoid over-loading server
    resize_images(path/o, max_size=400, dest=path/o)
images/Cristiano Ronaldo
Searching for 'Cristiano Ronaldo photo'
images/Lionel Messi
Searching for 'Lionel Messi photo'
/Users/jasonsuarez/anaconda3/envs/pydata-book/lib/python3.10/site-packages/PIL/Image.py:981: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(
# Check if any image is broken and delete it
failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
len(failed)
0

Preparing for Model Training

Setup of the pretrained model for fine-tuning. This includes specifying model parameters and configuring the learning environment.

dls = DataBlock(
    blocks=(ImageBlock, CategoryBlock),
    get_items=get_image_files,
    splitter=RandomSplitter(valid_pct=0.2, seed=42),
    get_y=parent_label,
    item_tfms=[Resize(192, method='squish')]
).dataloaders(path, bs=32)

dls.show_batch(max_n=6)

Training the Model

Fine-tuning the pretrained model on our dataset. This step involves training the model to classify images as either Messi or Cristiano Ronaldo accurately.

learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(3)
epoch train_loss valid_loss error_rate time
0 1.065711 0.397649 0.196078 00:02
epoch train_loss valid_loss error_rate time
0 0.476915 0.178397 0.058824 00:02
1 0.365285 0.163348 0.058824 00:02
2 0.277333 0.129455 0.058824 00:02 
results = learn.show_results()

Evaluating the Model

Assess the performance of the trained model on test data to ensure accurate classification.

is_goat,_,probs = learn.predict(PILImage.create('cristiano.jpg'))
print(f"Who is it?: {is_goat}.")
print(f"Probability it's the GOAT: {probs[0]:.4f}")
Is it the GOAT?: Cristiano Ronaldo.
Probability it's the GOAT: 0.9999
is_goat,_,probs = learn.predict(PILImage.create('messi.jpg'))
print(f"Who is it?: {is_goat}.")
print(f"Probability it's the GOAT: {probs[0]:.4f} 🙃")  # Picking the probability it's Ronaldo when it's Messi tells the joke
Who is it?: Lionel Messi.
Probability it's the GOAT: 0.0002 🙃
# Exports the model as a pickel file that may later be loaded, regardless of all the code that have served to train it.
# Have a look on the goat_detector_inference.ipynb file to see how it's done 
learn.export()

Conclusion

Summary of the training process and results. The trained model is now capable of classifying images between Messi and Cristiano Ronaldo with a certain accuracy.