*Memos:
- My post explains Caltech 256.
- My post explains Caltech101().
Caltech256() can use Caltech 256 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
transform(Optional-Default:None-Type:callable). - The 3rd argument is
target_transform(Optional-Default:None-Type:callable). - The 4th argument is
download(Optional-Default:False-Type:bool): *Memos:- If it's
True, the dataset is downloaded from the internet and extracted(unzipped) toroot. - If it's
Trueand the dataset is already downloaded, it's extracted. - If it's
Trueand the dataset is already downloaded and extracted, nothing happens. - It should be
Falseif the dataset is already downloaded and extracted because it's faster. - You can manually download and extract the dataset(
256_ObjectCategories.tar) from here todata/caltech256/.
- If it's
- About the label from the categories(classes) for the image indices, ak47(0) is 0~97, american-flag(1) is 98~194, backpack(2) is 195~345, baseball-bat(3) is 346~472, baseball-glove(4) is 473~620, basketball-hoop(5) is 621~710, bat(6) is 711~816, bathtub(7) is 817~1048, bear(8) is 1049~1150, beer-mug(9) is 1151~1244, etc.
from torchvision.datasets import Caltech256 my_data = Caltech256( root="data" ) my_data = Caltech256( root="data", transform=None, target_transform=None, download=False ) len(my_data) # 30607 my_data # Dataset Caltech256 # Number of datapoints: 30607 # Root location: data\caltech256 my_data.root # 'data/caltech256' print(my_data.transform) # None print(my_data.target_transform) # None my_data.download # <bound method Caltech256.download of Dataset Caltech256 # Number of datapoints: 30607 # Root location: data\caltech256> len(my_data.categories), my_data.categories # (257, # ['001.ak47', '002.american-flag', '003.backpack', '004.baseball-bat', # '005.baseball-glove', '006.basketball-hoop', '007.bat', '008.bathtub', # '009.bear', '010.beer-mug', '011.billiards', '012.binoculars', # ... # '254.greyhound', '255.tennis-shoes', '256.toad', '257.clutter']) my_data[0] # (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=499x278>, 0) my_data[1] # (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=268x218>, 0) my_data[2] # (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x186>, 0) my_data[98] # (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x328>, 1) my_data[195] # (<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=375x500>, 2) import matplotlib.pyplot as plt def show_images(data, main_title=None): plt.figure(figsize=(12, 6)) plt.suptitle(t=main_title, y=1.0, fontsize=14) ims = (0, 1, 2, 98, 195, 346, 473, 621, 711, 817) 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() show_images(data=my_data, main_title="my_data") 
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