- Caffe ：/home/cuiyan/lun/docomo_hashing/DSH/
- Script Code ：/home/cuiyan/lun/docomo_hashing/DSH/scripts/docomo/
- Docomo test Data ：/home/cuiyan/ting/Data/Docomo/cloth_data/
- Docomo train Data: /home/cuiyan/lun/docomo_train_image_set/
There is a brief outline of repository (README.txt) located under the path - /home/cuiyan/lun/docomo_hashing
1 Creating lmdb docomo training / testing data
- /home/cuiyan/lun/docomo_hashing/DSH/scripts/docomo/
create_docomo_test_lmdb.sh Create test lmdb data from 273634 image data create_pattern_lmdb.sh Generate train/test/validation lmdb for pattern data create_color_lmdb.sh Generate train/test/validation lmdb for color data create_type_lmdb.sh Generate train/test/validation lmdb for type data make_mean.sh prepare binaryproto mean from image dataset
2 Train network
- Please find train_.sh finetune_.sh under
/home/cuiyan/lun/docomo_hashing/DSH/TYPE/ /home/cuiyan/lun/docomo_hashing/DSH/PATTERN/ /home/cuiyan/lun/docomo_hashing/DSH/COLOR/
*Note: Logs and snapshots will be saved in each of the parent directories.
3 Extract binary features
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/home/cuiyan/lun/docomo_hashing/DSH/scripts/docomo/
extract_code.sh / extract_finetune_code.sh extract train data and labels binary code extract_code_docomo.sh extract 273634 test data binary code
4 Test network on partitions of data
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/home/cuiyan/lun/docomo_hashing/DSH/scripts/docomo/
test_map.m compute mean average precision on partition test set
5 Evaluate result of retrieval on docomo test dataset
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/home/cuiyan/lun/docomo_hashing/DSH/scripts/docomo/
docomo_prepare.m calculate hamming space distance from extract code docomo_get_retrieval.m sort 273634 image based on hamming space distance docomo_test_map.m evaluate mea average precision for top-N retrieval of 1000 queries
6 Repository Composition:
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model_prototxt - a number of neural network model prototxts for future usage and documentation. These network includes simple self devised CNN, AlexNet, ResNet-50, VGG-16, ResNet-18 and GoogLenet.
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model_weight - caffemodels that are likely to be reused.