This project presents code for extracting DELF features, which were introduced with the paper "Large-Scale Image Retrieval with Attentive Deep Local Features". It also contains code for the follow-up paper "Detect-to-Retrieve: Efficient Regional Aggregation for Image Search".

We also released pre-trained models based on the Google Landmarks dataset.

DELF is particularly useful for large-scale instance-level image recognition. It detects and describes semantic local features which can be geometrically verified between images showing the same object instance. The pre-trained models released here have been optimized for landmark recognition, so expect it to work well in this area. We also provide tensorflow code for building the DELF model, which could then be used to train models for other types of objects.

If you make use of this code, please consider citing the following papers:

"Large-Scale Image Retrieval with Attentive Deep Local Features",
H. Noh, A. Araujo, J. Sim, T. Weyand and B. Han,
Proc. ICCV'17

and/or

"Detect-to-Retrieve: Efficient Regional Aggregation for Image Search",
M. Teichmann*, A. Araujo*, M. Zhu and J. Sim,
Proc. CVPR'19

• [Apr'19] Check out our CVPR'19 paper: "Detect-to-Retrieve: Efficient Regional Aggregation for Image Search"
• [Jun'18] DELF achieved state-of-the-art results in a CVPR'18 image retrieval paper: Radenovic et al., "Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking".
• [Apr'18] DELF was featured in ModelDepot
• [Mar'18] DELF is now available in TF-Hub

We have two Google-Landmarks dataset versions:

• Initial version (v1) can be found here. In includes the Google Landmark Boxes which were described in the Detect-to-Retrieve paper.
• Second version (v2) has been released as part of two Kaggle challenges: Landmark Recognition and Landmark Retrieval. It can be downloaded from CVDF here.

If you make use of these datasets in your research, please consider citing the papers mentioned above.

To be able to use this code, please follow these instructions to properly install the DELF library.

We release several pre-trained models. See instructions in the following sections for examples on how to use the models.

DELF pre-trained on the Google-Landmarks dataset v1 (link). Presented in the CVPR'19 Detect-to-Retrieve paper. Boosts performance by ~4% mAP compared to ICCV'17 DELF model.

DELF pre-trained on Landmarks-Clean/Landmarks-Full dataset (link). Presented in the ICCV'17 DELF paper, model was trained on the dataset released by the DIR paper.

Faster-RCNN detector pre-trained on Google Landmark Boxes (link). Presented in the CVPR'19 Detect-to-Retrieve paper.

MobileNet-SSD detector pre-trained on Google Landmark Boxes (link). Presented in the CVPR'19 Detect-to-Retrieve paper.

Besides these, we also release pre-trained codebooks for local feature aggregation. See the Detect-to-Retrieve instructions for details.

Please follow these instructions. At the end, you should obtain a nice figure showing local feature matches, as:

Please follow these instructions. At the end, you should obtain a nice figure showing a detection, as:

Please follow these instructions. At the end, you should obtain image retrieval results on the Revisited Oxford/Paris datasets.

DELF/D2R code is located under the delf directory. There are two directories therein, protos and python.

This directory contains protobufs:

• aggregation_config.proto: protobuf for configuring local feature aggregation.
• box.proto: protobuf for serializing detected boxes.
• datum.proto: general-purpose protobuf for serializing float tensors.
• delf_config.proto: protobuf for configuring DELF extraction.
• feature.proto: protobuf for serializing DELF features.

This directory contains files for several different purposes:

• box_io.py, datum_io.py, feature_io.py are helper files for reading and writing tensors and features.
• delf_v1.py contains the code to create DELF models.
• feature_aggregation_extractor.py contains a module to perform local feature aggregation.
• feature_aggregation_similarity.py contains a module to perform similarity computation for aggregated local features.
• feature_extractor.py contains the code to extract features using DELF. This is particularly useful for extracting features over multiple scales, with keypoint selection based on attention scores, and PCA/whitening post-processing.

The subdirectory delf/python/examples contains sample scripts to run DELF feature extraction/matching, and object detection:

• delf_config_example.pbtxt shows an example instantiation of the DelfConfig proto, used for DELF feature extraction.
• extract_boxes.py enables object detection from a list of images.
• extract_features.py enables DELF extraction from a list of images.
• match_images.py supports image matching using DELF features extracted using extract_features.py.

The subdirectory delf/python/detect_to_retrieve contains sample scripts/configs related to the Detect-to-Retrieve paper:

• cluster_delf_features.py for local feature clustering.
• dataset.py for parsing/evaluating results on Revisited Oxford/Paris datasets.
• extract_aggregation.py for aggregated local feature extraction.
• extract_index_boxes_and_features.py for index image local feature extraction / bounding box detection on Revisited datasets.
• extract_query_features.py for query image local feature extraction on Revisited datasets.
• perform_retrieval.py for performing retrieval/evaluating methods using aggregated local features on Revisited datasets.
• delf_gld_config.pbtxt gives the DelfConfig used in Detect-to-Retrieve paper.
• index_aggregation_config.pbtxt, query_aggregation_config.pbtxt give AggregationConfig's for Detect-to-Retrieve experiments.

Besides these, other files in the different subdirectories contain tests for the various modules.

André Araujo (@andrefaraujo)

Detect-to-Retrieve code released.

Includes pre-trained models to detect landmark boxes, and DELF model pre-trained on Google Landmarks v1 dataset.

Thanks to contributors: André Araujo, Marvin Teichmann, Menglong Zhu, Jack Sim.

Initial release containing DELF-v1 code, including feature extraction and matching examples. Pre-trained DELF model from ICCV'17 paper is released.

Thanks to contributors: André Araujo, Hyeonwoo Noh, Youlong Cheng, Jack Sim.