ImageNet() can use ImageNet dataset as shown below:
*Memos:
- The 1st argument is
root(Required-Type:strorpathlib.Path). *An absolute or relative path is possible. - The 2nd argument is
split(Optional-Default:"train"-Type:str): *Memos:-
"train"(1,281,167 images) or"val"(50,000 images) can be set to it. -
"test"(100,000 images) isn't supported so I requested the feature on GitHub.
-
- There is
transformargument(Optional-Default:None-Type:callable). *transform=must be used. - There is
target_transformargument(Optional-Default:None-Type:callable). - There istransformargument(Optional-Default:None-Type:callable). *target_transform=must be used. - There is
loaderargument(Optional-Default:torchvision.datasets.folder.default_loader-Type:callable). *loader=must be used. - You have to manually download the dataset(ILSVRC2012_devkit_t12.tar.gz, ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar to
data/, then runningImageNet()extracts and loads the dataset. - About the label from the classes for the train and validation image indices respectively, tench&Tinca tinca(0) are 0~1299 and 0~49, goldfish&Carassius auratus(1) are 1300~2599 and 50~99, great white shark&white shark&man-eater&man-eating shark&Carcharodon carcharias(2) are 2600~3899 and 100~149, tiger shark&Galeocerdo cuvieri(3) are 3900~5199 and 150~199, hammerhead&hammerhead shark(4) are 5200~6499 and 200~249, electric ray&crampfish&numbfish&torpedo(5) are 6500~7799 and 250~299, stingray(6) is 7800~9099 and 250~299, cock(7) is 9100~10399 and 300~349, hen(8) is 10400~11699 and 350~399, ostrich&Struthio camelus(9) are 11700~12999 and 400~449, etc.
from torchvision.datasets import ImageNet from torchvision.datasets.folder import default_loader train_data = ImageNet( root="data" ) train_data = ImageNet( root="data", split="train", transform=None, target_transform=None, loader=default_loader ) val_data = ImageNet( root="data", split="val" ) len(train_data), len(val_data) # (1281167, 50000) train_data # Dataset ImageNet # Number of datapoints: 1281167 # Root location: D:/data # Split: train train_data.root # 'data' train_data.split # 'train' print(train_data.transform) # None print(train_data.target_transform) # None train_data.loader # <function torchvision.datasets.folder.default_loader(path: str) -> Any> len(train_data.classes), train_data.classes # (1000, # [('tench', 'Tinca tinca'), ('goldfish', 'Carassius auratus'), # ('great white shark', 'white shark', 'man-eater', 'man-eating shark', # 'Carcharodon carcharias'), ('tiger shark', 'Galeocerdo cuvieri'), # ('hammerhead', 'hammerhead shark'), ('electric ray', 'crampfish', # 'numbfish', 'torpedo'), ('stingray',), ('cock',), ('hen',), # ('ostrich', 'Struthio camelus'), ..., ('bolete',), ('ear', 'spike', # 'capitulum'), ('toilet tissue', 'toilet paper', 'bathroom tissue')]) train_data[0] # (<PIL.Image.Image image mode=RGB size=250x250>, 0) train_data[1] # (<PIL.Image.Image image mode=RGB size=200x150>, 0) train_data[2] # (<PIL.Image.Image image mode=RGB size=500x375>, 0) train_data[1300] # (<PIL.Image.Image image mode=RGB size=640x480>, 1) train_data[2600] # (<PIL.Image.Image image mode=RGB size=500x375>, 2) val_data[0] # (<PIL.Image.Image image mode=RGB size=500x375>, 0) val_data[1] # (<PIL.Image.Image image mode=RGB size=500x375>, 0) val_data[2] # (<PIL.Image.Image image mode=RGB size=500x375>, 0) val_data[50] # (<PIL.Image.Image image mode=RGB size=500x500>, 1) val_data[100] # (<PIL.Image.Image image mode=RGB size=679x444>, 2) import matplotlib.pyplot as plt def show_images(data, ims, main_title=None): plt.figure(figsize=(12, 6)) plt.suptitle(t=main_title, y=1.0, fontsize=14) for i, j in enumerate(iterable=ims, start=1): plt.subplot(2, 5, i) im, lab = data[j] plt.imshow(X=im) plt.title(label=lab) plt.tight_layout(h_pad=3.0) plt.show() train_ims = (0, 1, 2, 1300, 2600, 3900, 5200, 6500, 7800, 9100) val_ims = (0, 1, 2, 50, 100, 150, 200, 250, 300, 350) show_images(data=train_data, ims=train_ims, main_title="train_data") show_images(data=val_data, ims=val_ims, main_title="val_data") 
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