fast-reid/projects/FastRetri

FastRetri in FastReID

This project provides a strong baseline for fine-grained image retrieval.

Datasets Preparation

We use CUB200, CARS-196, Standford Online Products and In-Shop to evaluate the model's performance. You can do data management following dml_cross_entropy instruction.

Usage

Each dataset's config file can be found in projects/FastRetri/config, which you can use to reproduce the results of the repo.

For example, if you want to train with CUB200, you can run an experiment with cub.yml

python3 projects/FastRetri/train_net.py --config-file projects/FastRetri/config/cub.yml --num-gpus 4

Experiment Results

We refer to A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses as our baseline methods, and on top of it, we add some tricks, such as gem pooling. More details can be found in the config file and code.

CUB

Method	Pretrained	Recall@1	Recall@2	Recall@4	Recall@8	Recall@16	Recall@32
dml_cross_entropy	ImageNet	69.2	79.2	86.9	91.6	95.0	97.3
Fastretri	ImageNet	69.46	79.57	87.53	92.61	95.75	97.35

Cars-196

Method	Pretrained	Recall@1	Recall@2	Recall@4	Recall@8	Recall@16	Recall@32
dml_cross_entropy	ImageNet	89.3	93.9	96.6	98.4	99.3	99.7
Fastretri	ImageNet	92.31	95.99	97.60	98.63	99.24	99.62

Standford Online Products

Method	Pretrained	Recall@1	Recall@10	Recall@100	Recall@1000
dml_cross_entropy	ImageNet	81.1	91.7	96.3	98.8
Fastretri	ImageNet	82.46	92.56	96.78	98.95

In-Shop

Method	Pretrained	Recall@1	Recall@10	Recall@20	Recall@30	Recall@40	Recall@50
dml_cross_entropy	ImageNet	90.6	98.0	98.6	98.9	99.1	99.2
Fastretri	ImageNet	91.97	98.29	98.85	99.11	99.24	99.35