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This document introduces the preparation of ImageNet1k and flowers102
| Dataset | train dataset size | valid dataset size | category |
|---|---|---|---|
| flowers102 | 1k | 6k | 102 |
| ImageNet1k | 1.2M | 50k | 1000 |
Please follow the steps mentioned below to organize data, include train_list.txt and val_list.txt
# delimiter: "space"
ILSVRC2012_val_00000001.JPEG 65
...
After downloading data, please organize the data dir as below
PaddleClas/dataset/imagenet/
|_ train/
| |_ n01440764
| | |_ n01440764_10026.JPEG
| | |_ ...
| |_ ...
| |
| |_ n15075141
| |_ ...
| |_ n15075141_9993.JPEG
|_ val/
| |_ ILSVRC2012_val_00000001.JPEG
| |_ ...
| |_ ILSVRC2012_val_00050000.JPEG
|_ train_list.txt
|_ val_list.txt
Download Data then decompress:
jpg/
setid.mat
imagelabels.mat
Please put all the files under PaddleClas/dataset/flowers102
generate generate_flowers102_list.py and train_list.txt和val_list.txt
python generate_flowers102_list.py jpg train > train_list.txt
python generate_flowers102_list.py jpg valid > val_list.txt
Please organize data dir as below
PaddleClas/dataset/flowers102/
|_ jpg/
| |_ image_03601.jpg
| |_ ...
| |_ image_02355.jpg
|_ train_list.txt
|_ val_list.txt
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