Deep Local and Global Image Features

This project presents code for deep local and global image feature methods, which are particularly useful for the computer vision tasks of instance-level recognition and retrieval. These were introduced in the DELF, Detect-to-Retrieve, DELG and Google Landmarks Dataset v2 papers.

We provide Tensorflow code for building and training models, and python code for image retrieval and local feature matching. Pre-trained models for the landmark recognition domain are also provided.

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

DELF:

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

Detect-to-Retrieve:

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

DELG:

"Unifying Deep Local and Global Features for Image Search",
B. Cao*, A. Araujo* and J. Sim,
arxiv:2001.05027

GLDv2:

"Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval",
T. Weyand*, A. Araujo*, B. Cao and J. Sim,
Proc. CVPR'20

News

[Apr'20] Check out our CVPR'20 paper: "Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval"
[Jan'20] Check out our new paper: "Unifying Deep Local and Global Features for Image Search"
[Jun'19] DELF achieved 2nd place in CVPR Visual Localization challenge (Local Features track). See our slides here.
[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

Datasets

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. See also the CVPR'20 paper on this new dataset version.

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

Installation

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

Quick start

Pre-trained models

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 Detect-to-Retrieve paper. Boosts performance by ~4% mAP compared to ICCV'17 DELF model.

DELG pre-trained on the Google-Landmarks dataset v1 (link). Presented in the DELG paper.

RN101-ArcFace pre-trained on the Google-Landmarks dataset v2 (train-clean) (link). Presented in the GLDv2 paper.

DELF pre-trained on Landmarks-Clean/Landmarks-Full dataset (link). Presented in the 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 Detect-to-Retrieve paper.

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

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

DELF extraction and matching

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

DELF training

Please follow these instructions.

DELG

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

GLDv2 baseline

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

Landmark detection

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

Detect-to-Retrieve

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

Code overview

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

`delf/protos`

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/DELG extraction.
feature.proto: protobuf for serializing DELF features.

`delf/python`

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 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.
detector.py is a module to construct an object detector function.
extract_boxes.py enables object detection from a list of images.
extract_features.py enables DELF extraction from a list of images.
extractor.py is a module to construct a DELF/DELG local feature extraction function.
match_images.py supports image matching using DELF features extracted using extract_features.py.

The subdirectory delf/python/delg contains sample scripts/configs related to the DELG paper:

delg_gld_config.pbtxt gives the DelfConfig used in DELG paper.
extract_features.py for local+global feature extraction on Revisited datasets.
perform_retrieval.py for performing retrieval/evaluating methods on Revisited datasets.

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

aggregation_extraction.py is a library to extract/save feature aggregation.
boxes_and_features_extraction.py is a library to extract/save boxes and DELF features.
cluster_delf_features.py for local feature clustering.
dataset.py for parsing/evaluating results on Revisited Oxford/Paris datasets.
delf_gld_config.pbtxt gives the DelfConfig used in Detect-to-Retrieve paper.
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.
image_reranking.py is a module to re-rank images with geometric verification.
perform_retrieval.py for performing retrieval/evaluating methods using aggregated local features on Revisited datasets.
index_aggregation_config.pbtxt, query_aggregation_config.pbtxt give AggregationConfig's for Detect-to-Retrieve experiments.

The subdirectory delf/python/google_landmarks_dataset contains sample scripts/modules for computing GLD metrics / reproducing results from the GLDv2 paper:

compute_recognition_metrics.py performs recognition metric computation given input predictions and solution files.
compute_retrieval_metrics.py performs retrieval metric computation given input predictions and solution files.
dataset_file_io.py is a module for dataset-related file IO.
metrics.py is a module for GLD metric computation.
rn101_af_gldv2clean_config.pbtxt gives the DelfConfig used in the ResNet101-ArcFace (trained on GLDv2-train-clean) baseline used in the GLDv2 paper.

The subdirectory delf/python/training contains sample scripts/modules for performing DELF training:

datasets/googlelandmarks.py is the dataset module used for training.
model/delf_model.py is the model module used for training.
model/export_model.py is a script for exporting trained models in the format used by the inference code.
model/export_model_utils.py is a module with utilities for model exporting.
model/resnet50.py is a module with a backbone RN50 implementation.
build_image_dataset.py converts downloaded dataset into TFRecords format for training.
train.py is the main training script.

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

Maintainers

André Araujo (@andrefaraujo)

Release history

May, 2020

Codebase is now Python3-first
DELG model/code released
GLDv2 baseline model released

Thanks to contributors: Barbara Fusinska and André Araujo.

April, 2020 (version 2.0)

Initial DELF training code released.
Codebase is now fully compatible with TF 2.1.

Thanks to contributors: Arun Mukundan, Yuewei Na and André Araujo.

April, 2019

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.

October, 2017

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.