本教程演示了如何使用FastAI库训练一个图像分类模型，区分猫和狗。 我们将逐步进行，从数据准备到模型训练和使用。
步骤一：数据准备
<code class="language-python">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')</code><code class="language-python">urls = search_images('dog photos', max_images=1)
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)</code>同样地，我们下载一张猫的图片：
<code class="language-python">download_url(search_images('cat photos', max_images=1)[0], 'cat.jpg', show_progress=False)
Image.open('cat.jpg').to_thumb(256,256)</code>dog_or_not/dog和dog_or_not/cat文件夹中。 同时，我们调整图像大小以提高效率。<code class="language-python">searches = 'dog', 'cat'
path = Path('dog_or_not')
for o in searches:
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)</code><code class="language-python">failed = verify_images(get_image_files(path)) failed.map(Path.unlink)</code>
步骤二：模型训练
DataBlock创建DataLoader，用于加载和处理图像数据。<code class="language-python">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)</code><code class="language-python">learn = vision_learner(dls, resnet50, metrics=error_rate) learn.fine_tune(3)</code>
步骤三：模型使用
<code class="language-python">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}")</code>输出结果：
This is a: dog. Probability it's a dog: 1.0000
这个教程展示了如何利用FastAI快速构建一个简单的图像分类模型。 记住，模型的准确性取决于训练数据的质量和数量。
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