1、define a function of searching images
import os
iskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '')
if iskaggle:
!pip install -Uqq fastai 'duckduckgo_search>=6.2'
from duckduckgo_search import DDGS
from fastcore.all import *
import time, json
def search_images(keywords, max_images=200):
return L(DDGS().images(keywords, max_results=max_images)).itemgot('image')
2、 search for a dog photo and get URLs from a search
urls = search_images('dog photos', max_images=1)
3、download an image and take a look at it
from fastdownload import download_url
dest = 'dog.jpg'
download_url(urls[0], dest, show_progress=False)
from fastai.vision.all import *
im = Image.open(dest)
im.to_thumb(256,256)
4、do the same thing for a cat photo
download_url(search_images('cat photos', max_images=1)[0], 'cat.jpg', show_progress=False)
Image.open('cat.jpg').to_thumb(256,256)
5、grab a few examples of each of dog and cat photos, and save each group of photos to a different folder
searches = 'dog', 'cat'
path = Path('dog_or_not')
for o in searches:
# make sub dirs in dog_or_not
dest = (path/o)
dest.mkdir(exist_ok=True, parents=True)
download_images(dest, urls=search_images(f'{o} photo'))
time.sleep(5)
resize_images(path/o, max_size=400, dest=path/o)
6、remove the photos which might not be downloaded correctly causing our model training to fail
failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
1、creat the dataloaders using a DataBlock
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)
2、use the pretrained model and finetune it on our dataset
learn = vision_learner(dls, resnet50, metrics=error_rate)
learn.fine_tune(3)
1、Use the dog photo that we downloaded at the start to see what our model thinks about
is_dog,_,probs = learn.predict(PILImage.create('dog.jpg'))
print(f'This is a: {is_dog}.')
print(f"Probability it's a dog: {probs[1]:.4f}")
This is a: dog.
Probability it’s a dog: 1.0000
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