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# DELF: DEep Local Features

This project presents code for extracting DELF features, which were introduced
with the paper
["Large-Scale Image Retrieval with Attentive Deep Local Features"](https://arxiv.org/abs/1612.06321).
It also contains code for the follow-up paper
["Detect-to-Retrieve: Efficient Regional Aggregation for Image Search"](https://arxiv.org/abs/1812.01584).

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

## News

-   [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).

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)

### 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 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.

## Maintainers

André Araujo (@andrefaraujo)

## Release history

### 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.