NASCAR or F1 Car Classifier

Project

Author

Dylan Gallagher

Published

September 1, 2023

!pip install -Uqq fastai duckduckgo_search

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from duckduckgo_search import ddg_images
from fastcore.all import *

def search_images(term, max_images=30):
    print(f"Searching for '{term}'")
    return L(ddg_images(term, max_results=max_images)).itemgot('image')

urls = search_images('nascar photos', max_images=1)
urls[0]

Searching for 'nascar photos'

/usr/local/lib/python3.10/dist-packages/duckduckgo_search/compat.py:60: UserWarning: ddg_images is deprecated. Use DDGS().images() generator
  warnings.warn("ddg_images is deprecated. Use DDGS().images() generator")
/usr/local/lib/python3.10/dist-packages/duckduckgo_search/compat.py:64: UserWarning: parameter page is deprecated
  warnings.warn("parameter page is deprecated")
/usr/local/lib/python3.10/dist-packages/duckduckgo_search/compat.py:66: UserWarning: parameter max_results is deprecated
  warnings.warn("parameter max_results is deprecated")

'https://heavy.com/wp-content/uploads/2020/11/GettyImages-1283397584-e1604803312517.jpg?quality=65&strip=all'

from fastdownload import download_url
dest = 'nascar.jpg'
download_url(urls[0], dest, show_progress=False)

from fastai.vision.all import *
im = Image.open(dest)
im.to_thumb(256,256)

download_url(search_images('f1 photos', max_images=1)[0], 'f1.jpg', show_progress=False)
Image.open('f1.jpg').to_thumb(256,256)

Searching for 'f1 photos'

/usr/local/lib/python3.10/dist-packages/duckduckgo_search/compat.py:60: UserWarning: ddg_images is deprecated. Use DDGS().images() generator
  warnings.warn("ddg_images is deprecated. Use DDGS().images() generator")
/usr/local/lib/python3.10/dist-packages/duckduckgo_search/compat.py:64: UserWarning: parameter page is deprecated
  warnings.warn("parameter page is deprecated")
/usr/local/lib/python3.10/dist-packages/duckduckgo_search/compat.py:66: UserWarning: parameter max_results is deprecated
  warnings.warn("parameter max_results is deprecated")

searches = 'f1', 'nascar'
path = Path('race_series')
from time import sleep

for o in searches:
    dest = (path/o)
    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
    download_images(dest, urls=search_images(f'{o} old photo'))
    sleep(10)
    resize_images(path/o, max_size=400, dest=path/o)

Searching for 'f1 photo'
Searching for 'f1 old photo'
Searching for 'nascar photo'
Searching for 'nascar old photo'

failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
len(failed)

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)

learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(3)

/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
  warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
100%|██████████| 44.7M/44.7M [00:00<00:00, 71.4MB/s]

epoch	train_loss	valid_loss	error_rate	time
0	1.431362	0.930054	0.300000	00:09

epoch	train_loss	valid_loss	error_rate	time
0	0.660739	0.551799	0.250000	00:13
1	0.482893	0.117785	0.000000	00:14
2	0.360152	0.058476	0.000000	00:13

is_f1,_,probs = learn.predict(PILImage.create('f1.jpg'))
print(f"This is a: {is_f1}.")
print(f"Probability it's a f1 car: {probs[0]:.4f}")

This is a: f1.
Probability it's a f1 car: 0.9282

learn.export("model.pkl")