# Deep Local and Global Image Features

[![TensorFlow 2.1](https://img.shields.io/badge/tensorflow-2.1-brightgreen)](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

This project presents code for extracting local and global image features, which
are particularly useful for large-scale instance-level image recognition. These
were introduced in the [DELF](https://arxiv.org/abs/1612.06321),
[Detect-to-Retrieve](https://arxiv.org/abs/1812.01584) and
[DELG](https://arxiv.org/abs/2001.05027) papers.

We also released pre-trained models based on the
[Google Landmarks dataset](https://www.kaggle.com/google/google-landmarks-dataset).

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 and training models.

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

DELF:
[![Paper](http://img.shields.io/badge/paper-arXiv.1612.06321-B3181B.svg)](https://arxiv.org/abs/1612.06321)

```
"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:
[![Paper](http://img.shields.io/badge/paper-arXiv.1812.01584-B3181B.svg)](https://arxiv.org/abs/1812.01584)

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

DELG:
[![Paper](http://img.shields.io/badge/paper-arXiv.2001.05027-B3181B.svg)](https://arxiv.org/abs/2001.05027)

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

## News

-   [Jan'20] Check out our new paper:
    ["Unifying Deep Local and Global Features for Image Search"](https://arxiv.org/abs/2001.05027)
-   [Jun'19] DELF achieved 2nd place in
    [CVPR Visual Localization challenge (Local Features track)](https://sites.google.com/corp/view/ltvl2019).
    See our slides
    [here](https://docs.google.com/presentation/d/e/2PACX-1vTswzoXelqFqI_pCEIVl2uazeyGr7aKNklWHQCX-CbQ7MB17gaycqIaDTguuUCRm6_lXHwCdrkP7n1x/pub?start=false&loop=false&delayms=3000).
-   [Apr'19] Check out our CVPR'19 paper:
    ["Detect-to-Retrieve: Efficient Regional Aggregation for Image Search"](https://arxiv.org/abs/1812.01584)
-   [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"](https://arxiv.org/abs/1803.11285).
-   [Apr'18] DELF was featured in
    [ModelDepot](https://modeldepot.io/mikeshi/delf/overview)
-   [Mar'18] DELF is now available in
    [TF-Hub](https://www.tensorflow.org/hub/modules/google/delf/1)

## Dataset

We have two Google-Landmarks dataset versions:

-   Initial version (v1) can be found
    [here](https://www.kaggle.com/google/google-landmarks-dataset). 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](https://www.kaggle.com/c/landmark-recognition-2019)
    and [Landmark Retrieval](https://www.kaggle.com/c/landmark-retrieval-2019).
    It can be downloaded from CVDF
    [here](https://github.com/cvdfoundation/google-landmark). See also
    [the CVPR'20 paper](https://arxiv.org/abs/2004.01804) 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](INSTALL_INSTRUCTIONS.md) 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](http://storage.googleapis.com/delf/delf_gld_20190411.tar.gz)). Presented
in the [CVPR'19 Detect-to-Retrieve paper](https://arxiv.org/abs/1812.01584).
Boosts performance by ~4% mAP compared to ICCV'17 DELF model.

**DELF pre-trained on Landmarks-Clean/Landmarks-Full dataset**
([link](http://storage.googleapis.com/delf/delf_v1_20171026.tar.gz)). Presented
in the [ICCV'17 DELF paper](https://arxiv.org/abs/1612.06321), model was trained
on the dataset released by the [DIR paper](https://arxiv.org/abs/1604.01325).

**Faster-RCNN detector pre-trained on Google Landmark Boxes**
([link](http://storage.googleapis.com/delf/d2r_frcnn_20190411.tar.gz)).
Presented in the
[CVPR'19 Detect-to-Retrieve paper](https://arxiv.org/abs/1812.01584).

**MobileNet-SSD detector pre-trained on Google Landmark Boxes**
([link](http://storage.googleapis.com/delf/d2r_mnetssd_20190411.tar.gz)).
Presented in the
[CVPR'19 Detect-to-Retrieve paper](https://arxiv.org/abs/1812.01584).

Besides these, we also release pre-trained codebooks for local feature
aggregation. See the
[Detect-to-Retrieve instructions](delf/python/detect_to_retrieve/DETECT_TO_RETRIEVE_INSTRUCTIONS.md)
for details.

### DELF extraction and matching

Please follow [these instructions](EXTRACTION_MATCHING.md). At the end, you
should obtain a nice figure showing local feature matches, as:

![MatchedImagesExample](delf/python/examples/matched_images_example.jpg)

### DELF training

Please follow [these instructions](delf/python/training/README.md).

### DELG

Please follow [these instructions](delf/python/delg/DELG_INSTRUCTIONS.md). At
the end, you should obtain image retrieval results on the Revisited Oxford/Paris
datasets.

### Landmark detection

Please follow [these instructions](DETECTION.md). At the end, you should obtain
a nice figure showing a detection, as:

![DetectionExample1](delf/python/examples/detection_example_1.jpg)

### Detect-to-Retrieve

Please follow
[these instructions](delf/python/detect_to_retrieve/DETECT_TO_RETRIEVE_INSTRUCTIONS.md).
At the end, you should obtain image retrieval results on the Revisited
Oxford/Paris datasets.

## Code overview

DELF/D2R 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 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 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:

-   `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.

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

### 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.