Comments (6)
layumi
commented on May 20, 2024
@yiboin Please check that you have downloaded the following files in this dir correctly.
https://github.com/layumi/2016_person_re-ID/tree/master/data
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yiboin
commented on May 20, 2024
@layumi 你好！我又重新把代码下载了一遍，一共20.81MB，然后我在matlab里，尝试直接加载数据load('D:\reid\2016_person_re-ID-master\data\imagenet-resnet-50-dag.mat') 时,显示 "错误使用 load
无法读取 MAT 文件"D:\reid\2016_person_re-ID-master\data\imagenet-resnet-50-dag.mat: 不是二进制 MAT 文件。请尝试执行 LOAD -ASCII 以便以文本形式读取" ？
还望指点，感谢！
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layumi
commented on May 20, 2024
@yiboin The size of the model is about 90~100MB. Please directly download the model in the dir.
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yiboin
commented on May 20, 2024
@layumi I am sorry to bother you again! I can only download the mat file of imagenet-resnet-50-dag.mat but not find "resnet52_market.mat","net-epoch-75.mat",and "vl_demo_svm_data.mat". Could you give me a link on BaiDu to download these files?
Thank you!!
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layumi
commented on May 20, 2024
@yiboin Thank you! It's my fault. Today I double-check the 'git clone'. I use the git-lfs to store the large models. But this reposity may be over data quota (download limitation).
Alternatively, you can download the complete codes from GoogleDriver or [BaiduYun] (https://pan.baidu.com/s/1mhKoQ4S). Sorry for my mistake.
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yiboin
commented on May 20, 2024
@layumi Thank you for the complete codes! I can run the demo without errors. Thanks!!
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Related Issues (20)
- undefined function or variable 'dagnn.Square' HOT 6
- run zzd_evaluation_res_faster.m and got NULL HOT 1
- About the share weights? HOT 2
- some issues about Oxford5k dataset and train order HOT 3
- Use 2016_person_re-ID train DukeMTMC HOT 5
- about the cuhk03 evaluation HOT 15
- download link for pre-trained models HOT 6
- Attempt to execute SCRIPT vl_nnconv as a function: HOT 1
- Implementation on Keras HOT 2
- Pytorch implementation of this paper HOT 4
- The variable "dagnn" or class "dagnn.DagNN.loadobj" is not defined. HOT 5
- Undefined function or variable 'net' HOT 5
- about training the cuhk03 dataset HOT 1
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- 可视化 HOT 2
- the ratio of classification loss and verification loss is 1/2 HOT 4
- the way of ResNet-50-Basel HOT 2
- how to get the result of single query and multi query HOT 3
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