carlosaguayo
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Cats vs Dogs classifier
Browse files- .DS_Store +0 -0
- README.md +55 -0
- config.json +7 -0
- preprocessor_config.json +15 -0
.DS_Store
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Binary file (6.15 kB). View file
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README.md
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---
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tags:
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- image-classification
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widget:
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- src: https://upload.wikimedia.org/wikipedia/commons/0/0c/About_The_Dog.jpg
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example_title: Dog-1
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- src: https://yt3.ggpht.com/ytc/AKedOLRvxGYSdEHqu0X4EYcJ2kq7BttRKBNpfwdHJf3FSg=s900-c-k-c0x00ffffff-no-rj
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example_title: Dog-2
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- src: https://upload.wikimedia.org/wikipedia/commons/c/c7/Tabby_cat_with_blue_eyes-3336579.jpg
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example_title: Cat-1
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- src: https://pixabay.com/get/g31cf3b945cf9b9144eb6c1ecf514b4db668875b75d0c615e0330aec74bef5edde11567ef4a6f5fdb61a828b8086a39d3a0e72fb326d78467786dcdde4e6fa23c5c4c309d0abc089a8663809c175aee22_1920.jpg
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example_title: Cat-2
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---
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# Classify Cats and Dogs
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VGG16 fine tuned to classify cats and dogs
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Notebook
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https://www.kaggle.com/carlosaguayo/cats-vs-dogs-transfer-learning-pre-trained-vgg16
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### How to use
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Here is how to use this model to classify an image as a cat or dog:
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```python
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from skimage import io
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import cv2
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import matplotlib.pyplot as plt
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from huggingface_hub import from_pretrained_keras
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%matplotlib inline
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ROWS, COLS = 150, 150
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model = from_pretrained_keras("carlosaguayo/cats_vs_dogs")
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img_url = 'https://upload.wikimedia.org/wikipedia/commons/0/0c/About_The_Dog.jpg'
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# img_url = 'https://upload.wikimedia.org/wikipedia/commons/c/c7/Tabby_cat_with_blue_eyes-3336579.jpg'
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img = io.imread(img_url)
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img = cv2.resize(img, (ROWS, COLS), interpolation=cv2.INTER_CUBIC)
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img = img / 255.0
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img = img.reshape(1,ROWS,COLS,3)
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prediction = model.predict(img)[0][0]
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if prediction >= 0.5:
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print('I am {:.2%} sure this is a Cat'.format(prediction))
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else:
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print('I am {:.2%} sure this is a Dog'.format(1-prediction))
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plt.imshow(img[0], 'Blues')
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plt.axis("off")
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plt.show()
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```
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config.json
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{
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"id2label": {
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"0": "Dog",
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"1": "Cat"
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},
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"num_channels": 3
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}
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preprocessor_config.json
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{
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"do_normalize": true,
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"do_resize": true,
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_std": [
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0.5,
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0.5,
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0.5
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],
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"size": 150
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}
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