Comments (7)
Hello, I am coming, about the dataset, first, you should download the original ILSVRC2012, specially, only downloading Training images (Task 1 & 2) and Validation images (all tasks) are enough:
In this project, you need to download the corresponding ImageNet-LT and Places-LT from here. Hence, the current structure of "./data" is:
|--data
|--ImageNet_LT_open
|--ILSVRC2010_val_00020390.JPEG
..
|--ImageNet_LT_open.txt
|--ImageNet_LT_test.txt
|--ImageNet_LT_train.txt
|--ImageNet_LT_val.txt
..
Then, we decompress files “ILSVRC2012_img_train.tar” and “ILSVRC2012_img_val.tar”. Note that, "ILSVRC2012_img_test.tar" is not used in this project. Now, we finish decompression (ILSVRC2012_img_train.tar -> train, ILSVRC2012_img_val.tar -> val_tar):
|--large_scale_dataset
|--ImageNet
|--train
|--val_tar
Then, run the following code to generate test dataset (i.e., val):
import os
from shutil import copy
root = "./oltr/data/ImageNet_LT/ImageNet_LT_test.txt"
source_root = "./large_scale_dataset/ImageNet/val_tar"
target_root = "./large_scale_dataset/ImageNet/val"
with open(root, 'r') as f:
while True:
line = f.readline()
if len(line) <= 1:
break
line = line.strip('\n')
line = line.split(' ')[0]
file_seq = line.split('/')
pre_holder = file_seq[1]
basename = file_seq[-1]
target = target_root + "/" + pre_holder
os.makedirs(target, exist_ok=True)
source = source_root + "/" + basename
os.makedirs(target, exist_ok=True)
copy(source, target + "/" + basename)
Finally, you can see the structure in ""./large_scale_dataset/ImageNet/":
|--large_scale_dataset
|--ImageNet
|--train
|--val_tar
|--val
And the "val" holder( see the following image) is the corresponding test dataset, the "train" holder contains both the training and validation datasets.
I try to look back upon full process of generating those holders, if it lacks something, please tell me.
from openlongtailrecognition-oltr.
Hello did you get an idea about the data arrangements? As I am having problem in validation dataset.
from openlongtailrecognition-oltr.
Hello, can you provide the link to download the imagenet2014 dataset?
from openlongtailrecognition-oltr.
Hello, can you provide the link to download the imagenet2014 dataset?
https://image-net.org/challenges/LSVRC/2014/2014-downloads.php
from openlongtailrecognition-oltr.
I also have problem about the arrangement of ILSVRC2014 in this project, it seems that author maybe use ILSVRC2012 rather than ILSVRC2014 ?
update 2023.4.20:
By downloading the ILSVRC2012 and splitting test dataset into the format in "ImageNet_LT_test.txt", the code runs successfully.
dataset holder
|--ImageNet
|--train
|--n01440764
..
|--val
|--n01440764
..
Note that, places365 keeps the original file structure, and renaming the holder is enough.
from openlongtailrecognition-oltr.
I also have problem about the arrangement of ILSVRC2014 in this project, it seems that author maybe use ILSVRC2012 rather than ILSVRC2014 ?
update 2023.4.20: By downloading the ILSVRC2012 and splitting test dataset into the format in "ImageNet_LT_test.txt", the code runs successfully.
dataset holder
|--ImageNet |--train |--n01440764 .. |--val |--n01440764 ..
Note that, places365 keeps the original file structure, and renaming the holder is enough.
Sorry to bother, could you tell how to split test dataset into the format in "ImageNet_LT_test.txt", since ILSVRC2012_img_test_v10102019.tar doesn't have the arctecture like test/n01440764
from openlongtailrecognition-oltr.
我对这个项目中 ILSVRC2014 的安排也有疑问,似乎作者可能使用 ILSVRC2012 而不是 ILSVRC2014 ?
2023.4.20更新:通过下载ILSVRC2012并将测试数据集拆分为“ImageNet_LT_test.txt”中的格式,代码运行成功。
数据集持有者|--ImageNet |--train |--n01440764 .. |--val |--n01440764 ..
我对这个项目中 ILSVRC2014 的安排也有疑问,似乎作者可能使用 ILSVRC2012 而不是 ILSVRC2014 ?
2023.4.20更新: 通过下载ILSVRC2012并将测试数据集拆分为“ImageNet_LT_test.txt”中的格式,代码运行成功。
数据集持有者
|--ImageNet |--train |--n01440764 .. |--val |--n01440764 ..
请注意,places365 保留了原始文件结构,重命名 holder
你好,打扰下,能分享下你是如何将数据集拆分成如下结构的吗
|--ImageNet
|--train
|--n01440764
..
|--val
|--n01440764
..
from openlongtailrecognition-oltr.
Related Issues (20)
- Reproducing OLTR results HOT 3
- Stage 2 multi GPU
- why fix all parameters except self attention parameters? HOT 4
- Table 2 results HOT 2
- Pretrained Weights for Places_LT?
- the use of fc layer HOT 2
- the accuracy of the train and val HOT 2
- how to compute centroids?
- Why the input dimension of the `fc_spatial` layer in `ModulatedAttLayer` is 7*7*in_channel? HOT 1
- Many_shot_accuracy_top1: nan on my own dataset HOT 1
- Revised F-measure results for other models in your paper
- Applications for face recognition
- Error when running stage_1.py under Places_LT
- Unable to reproduce baseline result on ImageNet-LT HOT 1
- BUG: stage1 test error!!
- Implementation on Inat-18
- About Class aware sampler
- The role of untrained FC(add_fc)
- The question about the version of Places_LT
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