Cannot get into the wbe page" http://www.xrce.xerox.com/Our-Research/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval"

[wangcx2018]

Hi,I have tried to open the wbe page you supplied, but failed. Can you give us details of how to run the source code in README file? For example,how to set the parameters such as dataset path? I really hope you can list them clearly one step by step,thank you very much!

[zlaskar]

Does this work for you? It is a README from the original code-base from Gordo et. al

On Wed, Jun 20, 2018 at 10:18 AM, wangcx2018 ***@***.***> wrote: Hi,I have tried to open the wbe page you supplied, but failed. Can you give us details of how to run the source code in README file? For example,how to set the parameters such as dataset path? I really hope you can list them clearly one step by step,thank you very much! — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#1>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ANi_SznGsJcJErMP51HEWX_iHxhJhzbWks5t-fdDgaJpZM4Uuu1F> .

This package contains the pretrained ResNet101 model and evaluation script for the method proposed in the following papers: Deep Image Retrieval: Learning global representations for image search. A. Gordo, J. Almazan, J. Revaud, and D. Larlus. In ECCV, 2016 End-to-end Learning of Deep Visual Representations for Image Retrieval. A. Gordo, J. Almazan, J. Revaud, and D. Larlus. CoRR abs/1610.07940, 2016 Dependencies: - Caffe - Region of Interest pooling layer (ROIPooling). This is the same layer used by fast RCNN and faster RCNN. A C++ implementation can be found in BVLC/caffe#4163 - L2-normalization layer (Normalize). Implemented in C++ in https://github.com/happynear/caffe-windows. As an alternative, we provide a python implementation of this layer that produces the same results, but is less efficient and does not implement backpropagation. Datasets: The evaluation script is prepared to work on the Oxford 5k and Paris 6k datasets. To set up the datasets: mkdir datasets cd datasets # Evaluation script mkdir evaluation cd evaluation wget http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/compute_ap.cpp sed -i '6i#include <cstdlib>' compute_ap.cpp # Add cstdlib, as some compilers will produce an error otherwise g++ -o compute_ap compute_ap.cpp cd .. # Oxford: mkdir -p Oxford cd Oxford mkdir jpg lab wget http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/oxbuild_images.tgz tar -xzf oxbuild_images.tgz -C jpg wget http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/gt_files_170407.tgz tar -xzf gt_files_170407.tgz -C lab cd .. # Paris: mkdir -p Paris cd Paris mkdir jpg lab tmp # Images are in a different folder structure, need to move them around wget http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_1.tgz wget http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_2.tgz tar -xzf paris_1.tgz -C tmp tar -xzf paris_2.tgz -C tmp find tmp -type f -exec mv {} jpg/ \; rm -rf tmp wget http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_120310.tgz tar -xzf paris_120310.tgz -C lab cd .. cd .. Usage: (0)[9336]$ python test.py usage: test.py [-h] --gpu GPU --S S --L L --proto PROTO --weights WEIGHTS --dataset DATASET --dataset_name DATASET_NAME --eval_binary EVAL_BINARY --temp_dir TEMP_DIR [--multires] [--aqe AQE] [--dbe DBE] G: gpu id S: size to resize the largest side of the images to. The model is trained with S=800, but different values may work better depending on the task. L: number of levels of the rigid grid. Model was trained with L=2, but different levels (e.g. L=1 or L=3) may work better on other tasks. PROTO: path to the prototxt. There are two prototxts included. deploy_resnet101.prototxt relies on caffe being compiled with the normalization layer. deploy_resnet101_normpython.prototxt does not have that requirement as it relies on the python implementation, but it may be slower as it is done on the cpu and does not implement backpropagation. WEIGHTS: path to the caffemodel DATASET: path to the dataset, for Oxford and Paris it is the directory that contains the jpg and lab folders. DATASET_NAME: either Oxford or Paris EVAL_BINARY: path to the compute_ap binary provided with Oxford and Paris used to compute the ap scores TEMP_DIR: a temporary directory to store features and scores Note that this model does not implement the region proposal network. Examples (adjust paths as necessary): * Rigid grid, no multiresolution, no query expansion or database side feature augmentation: python test.py --gpu 0 --S 800 --L 2 --proto deploy_resnet101_normpython.prototxt --weights model.caffemodel --dataset datasets/Oxford --eval_binary datasets/evaluation/compute_ap --temp_dir tmp --dataset_name Oxford Expected accuracy: 84.09 python test.py --gpu 0 --S 800 --L 2 --proto deploy_resnet101_normpython.prototxt --weights model.caffemodel --dataset datasets/Paris --eval_binary datasets/evaluation/compute_ap --temp_dir tmp --dataset_name Paris Expected accuracy: 93.57 * Rigid grid, multiresolution, no query expansion or database side feature augmentation: python test.py --gpu 0 --S 800 --L 2 --proto deploy_resnet101_normpython.prototxt --weights model.caffemodel --dataset datasets/Oxford --eval_binary datasets/evaluation/compute_ap --temp_dir tmp --dataset_name Oxford --multires Expected accuracy: 86.07 python test.py --gpu 0 --S 800 --L 2 --proto deploy_resnet101_normpython.prototxt --weights model.caffemodel --dataset datasets/Paris --eval_binary datasets/evaluation/compute_ap --temp_dir tmp --dataset_name Paris --multires Expected accuracy: 94.53 * Rigid grid, multiresolution, query expansion (k=1) and database side feature augmentation (k=20): python test.py --gpu 0 --S 800 --L 2 --proto deploy_resnet101_normpython.prototxt --weights model.caffemodel --dataset datasets/Oxford --eval_binary datasets/evaluation/compute_ap --temp_dir tmp --dataset_name Oxford –multires --aqe 1 --dbe 20 Expected accuracy: 94.68 python test.py --gpu 0 --S 800 --L 2 --proto deploy_resnet101_normpython.prototxt --weights model.caffemodel --dataset datasets/Paris --eval_binary datasets/evaluation/compute_ap --temp_dir tmp --dataset_name Paris –multires --aqe 1 --dbe 20 Expected accuracy: 96.58 If you use this model please cite: @inproceedings{Gordo2016a, title={Deep Image Retrieval: Learning global representations for image search}, author={Albert Gordo and Jon Almazan and Jerome Revaud and Diane Larlus}, booktitke={ECCV}, year={2016} } @Article{Gordo2016b, title={End-to-end Learning of Deep Visual Representations for Image Retrieval} author={Albert Gordo and Jon Almazan and Jerome Revaud and Diane Larlus}, journal={CoRR abs/1610.07940}, year={2016} } Please see LICENSE.txt for the license information.