The Google Landmark Dataset V2 is currently the largest publicly image retrieval and recogntion dataset, including 4M training data, more than 100,000 query images and nearly 1M index data. The large amounts of images in training dataset is the driving force of the generalizability of machine learning models. Here, we release our trained models in Google Landmark 2019 Competition, the detail of our solution can refer to our paper [[link](https://arxiv.org/pdf/1906.03990.pdf)].
## Retrieval Models
We fine-tune four convolutional neural networks to extract our global image descriptors. The four convolutional backbones include ResNet152, ResNet200, SE ResNeXt152 and InceptionV4. We choose arcmargin and npairs as our training loss, We train these models using Google Landmark V2 training set and index set. You can download trained models here. The training code can refer to metric learning [[link](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/metric_learning)].
In addition, we also train a classification model based on ResNet152 with ~4M Google Landmark V2 training set. ([res152_softmax_v1](https://landmark.gz.bcebos.com/res152_softmax_v1.tar))
The taining code can refer to image classification [[link](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification)].
## Recognition Models
There are three models in our recognition solution.
1.[res152_arcmargin](https://landmark.gz.bcebos.com/res152_arcmargin.tar): Retrieval model based on Resnet152 and arcmargin which is the same as in the retrieval task.
2.[res152_softmax_v2](https://landmark.gz.bcebos.com/res152_softmax_v2.tar): Classification model based on Resnet152 and softmax with ~3M Google Landmark V2 tidied training set. The training code can refer to image classification [[link](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification)].
3.[res50_oid_v4_detector](https://landmark.gz.bcebos.com/res50_oid_v4_detector.tar): Object detector model for the non-landmark images filtering. The mAP of this model is ~0.55 on the OID V4 track (public LB). The training code can refer to RCNN detector [[link](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/rcnn)].
## Environment
Cudnn >= 7, CUDA 9, PaddlePaddle version >= 1.3, python version 2.7
## Inference
### 1.Compile paddle infer so and predict with binary model
There are two different type of models in PaddlePaddle: train model and binary model. Predict with the binary model is more efficient. Thus, at first we compile paddle infer so and convert train model to binary model.
(1) Compile paddle infer so
Please refer the README.md in pypredict.
(2) Convert train model to binary model
```
pushd inference
sh convert.sh
```
### 2.Extract retrieval feature and calculate cosine distance
In the folder ./inference/test_data, there are four images, 0.jpg and 1.jpg are same landmark images, 2.jpg is another landmark image, 3.jpg is a non-lamdnark image.
We will extract the features of these images, and calculate the cosine distances between 0.jpg and 1.jpg, 2.jpg, 3.jpg.
You will get the inference detetor bounding box and classes. The class mapping file: pretrained_models/res50_oid_v4_detector/cls_name_idx_map_openimagev4_500.txt
