Oxford Pets

# Generated pet images for deterministic, rights-safe execution.
from pathlib import Path
from PIL import Image
import numpy as np

image_root = Path("images")
image_root.mkdir(parents=True, exist_ok=True)
for breed_index, breed in enumerate(("Abyssinian", "Maine_Coon", "beagle", "pug")):
    for image_index in range(1, 4):
        yy, xx = np.mgrid[:64, :64]
        pixels = np.zeros((64, 64, 3), dtype=np.uint8)
        pixels[..., 0] = (xx * (2 + breed_index) + 23 * image_index) % 256
        pixels[..., 1] = (yy * (3 + image_index) + 31 * breed_index) % 256
        pixels[..., 2] = ((xx + yy) * 2 + 47 * breed_index) % 256
        Image.fromarray(pixels).save(image_root / f"{breed}_{image_index}.jpg")
print("Prepared 12 generated pet images")

import matplotlib.pyplot as plt
import os
from PIL import Image
import numpy as np

def display_images(images, titles=None, fontsize=12):
    images = list(images)[:24]
    if not images:
        return
    columns = min(len(images), 6)
    rows = (len(images) + columns - 1) // columns
    fig, axes = plt.subplots(rows, columns, squeeze=False, figsize=(columns * 2.5, rows * 2.5))
    flat_axes = axes.reshape(-1)
    for index, image in enumerate(images):
        flat_axes[index].imshow(image)
        flat_axes[index].axis("off")
        if titles is not None and index < len(titles):
            flat_axes[index].set_title(titles[index], fontsize=fontsize)
    for axis in flat_axes[len(images):]:
        axis.axis("off")
    plt.tight_layout()
    plt.show()

fnames = os.listdir('images')[:5]
display_images([Image.open(os.path.join('images',x)) for x in fnames],titles=fnames,fontsize=30)

for fn in os.listdir('images'):
    cls = fn[:fn.rfind('_')].lower()
    os.makedirs(os.path.join('images',cls),exist_ok=True)
    os.replace(os.path.join('images',fn),os.path.join('images',cls,fn))

num_classes = len(os.listdir('images'))
num_classes

# PREPARE THE DATASET

# SPLIT INTO TRAIN-TEST DATASETS

# DEFINE DATA LOADERS if needed

# [OPTIONAL] Plot the dataset

# DEFINE NEURAL NETWORK ARCHITECTURE

# TRAIN THE NEURAL NETWORK

# PLOT THE RESULT: Train and Test Accuracy

# LOAD THE DATASET
# Perform standard transformations for VGG-16/VGG-19 if needed

# vgg = ...

# BUILD MODEL for your problem with your own linear layers

# MAKE VGG Layers not trainable

# TRAIN THE MODEL

# CALCULATE TOP-3 Accuracy of the model