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*My post explains Oxford 102 Flower.
Flowers102() can use Oxford 102 Flower dataset as shown below:
*Memos:
from torchvision.datasets import Flowers102 train_data = Flowers102( root="data" ) train_data = Flowers102( root="data", split="train", transform=None, target_transform=None, download=False ) val_data = Flowers102( root="data", split="val" ) test_data = Flowers102( root="data", split="test" ) len(train_data), len(val_data), len(test_data) # (1020, 1020, 6149) train_data # Dataset Flowers102 # Number of datapoints: 1020 # Root location: data # split=train train_data.root # 'data' train_data._split # 'train' print(train_data.transform) # None print(train_data.target_transform) # None train_data.download # <bound method flowers102.download of dataset flowers102 number datapoints: root location: data split="train"> len(set(train_data._labels)), train_data._labels # (102, # [0, 0, 0, ..., 1, ..., 2, ..., 3, ..., 4, ..., 5, ..., 6, ..., 101]) train_data[0] # (<pil.image.image image mode="RGB" size="754x500">, 0) train_data[1] # (<pil.image.image image mode="RGB" size="624x500">, 0) train_data[2] # (<pil.image.image image mode="RGB" size="667x500">, 0) train_data[10] # (<pil.image.image image mode="RGB" size="500x682">, 1) train_data[20] # (<pil.image.image image mode="RGB" size="667x500">, 2) val_data[0] # (<pil.image.image image mode="RGB" size="606x500">, 0) val_data[1] # (<pil.image.image image mode="RGB" size="667x500">, 0) val_data[2] # (<pil.image.image image mode="RGB" size="500x628">, 0) val_data[10] # (<pil.image.image image mode="RGB" size="500x766">, 1) val_data[20] # (<pil.image.image image mode="RGB" size="624x500">, 2) test_data[0] # (<pil.image.image image mode="RGB" size="523x500">, 0) test_data[1] # (<pil.image.image image mode="RGB" size="666x500">, 0) test_data[2] # (<pil.image.image image mode="RGB" size="595x500">, 0) test_data[20] # (<pil.image.image image mode="RGB" size="500x578">, 1) test_data[60] # (<pil.image.image image mode="RGB" size="500x625">, 2) import matplotlib.pyplot as plt def show_images(data, ims, main_title=None): plt.figure(figsize=(10, 5)) plt.suptitle(t=main_title, y=1.0, fontsize=14) for i, j in enumerate(ims, start=1): plt.subplot(2, 5, i) im, lab = data[j] plt.imshow(X=im) plt.title(label=lab) plt.tight_layout() plt.show() train_ims = (0, 1, 2, 10, 20, 30, 40, 50, 60, 70) val_ims = (0, 1, 2, 10, 20, 30, 40, 50, 60, 70) test_ims = (0, 1, 2, 20, 60, 80, 116, 161, 186, 206) show_images(data=train_data, ims=train_ims, main_title="train_data") show_images(data=train_data, ims=val_ims, main_title="val_data") show_images(data=test_data, ims=test_ims, main_title="test_data") </pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></bound>
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