Dogs classification with Deep Metric Learning using some popular losses

Last update: Nov 9, 2022

Overview

Tsinghua Dogs classification with
Deep Metric Learning

1. Introduction

Tsinghua Dogs dataset

Tsinghua Dogs is a fine-grained classification dataset for dogs, over 65% of whose images are collected from people's real life. Each dog breed in the dataset contains at least 200 images and a maximum of 7,449 images. For more info, see dataset's homepage.

Following is the brief information about the dataset:

Number of categories: 130
Number of training images: 65228
Number of validating images: 5200

Variation in Tsinghua Dogs dataset. (a) Great Danes exhibit large variations in appearance, while (b) Norwich terriers and (c) Australian terriers are quite similar to each other. (Source)

Deep metric learning

Deep metric learning (DML) aims to measure the similarity among samples by training a deep neural network and a distance metric such as Euclidean distance or Cosine distance. For fine-grained data, in which the intra-class variances are larger than inter-class variances, DML proves to be useful in classification tasks.

Goal

In this projects, I use deep metric learning to classify dog images in Tsinghua Dogs dataset. Those loss functions are implemented:

I also evaluate models' performance on some common metrics:

2. Benchmarks

Architecture: Resnet-50 for feature extractions.
Embedding size: 128.
Batch size: 48.
Number of epochs: 100.
Online hard negatives mining.
Augmentations:
- Random horizontal flip.
- Random brightness, contrast and saturation.
- Random affine with rotation, scale and translation.

	MAP	P@1	P@5	P@10	Top-5	NMI
Triplet loss	73.85%	74.66%	73.90	73.00%	93.76%	0.82
Proxy-NCA loss	89.10%	90.26%	89.28%	87.76%	99.39%	0.98
Proxy-anchor loss
Soft-triple loss

3. Visualization

Proxy-NCA loss

Confusion matrix on validation set

T-SNE on validation set

Similarity matrix of some images in validation set

Each cell represent the L2 distance between 2 images.
The closer distance to 0 (blue), the more similar.
The larger distance (green), the more dissimilar.

Triplet loss

Confusion matrix on validation set

T-SNE on validation set

Similarity matrix of some images in validation set

Each cell represent the L2 distance between 2 images.
The closer distance to 0 (blue), the more similar.
The larger distance (green), the more dissimilar.

4. Train

4.1 Install dependencies

# Create conda environment
conda create --name dml python=3.7 pip
conda activate dml

# Install pytorch and torchvision
conda install -n dml pytorch torchvision cudatoolkit=10.2 -c pytorch

# Install faiss for indexing and calulcating accuracy
# https://github.com/facebookresearch/faiss
conda install -n dml faiss-gpu cudatoolkit=10.2 -c pytorch

# Install other dependencies
pip install opencv-python tensorboard torch-summary torch_optimizer scikit-learn matplotlib seaborn requests ipdb flake8 pyyaml

4.2 Prepare Tsinghua Dogs dataset

PYTHONPATH=./ python src/scripts/prepare_TsinghuaDogs.py --output_dir data/

Directory data should be like this:

data/
└── TsinghuaDogs
    ├── High-Annotations
    ├── high-resolution
    ├── TrainAndValList
    ├── train
    │   ├── 561-n000127-miniature_pinscher
    │   │   ├── n107028.jpg
    │   │   ├── n107031.jpg
    │   │   ├── ...
    │   │   └── n107218.jp
    │   ├── ...
    │   ├── 806-n000129-papillon
    │   │   ├── n107440.jpg
    │   │   ├── n107451.jpg
    │   │   ├── ...
    │   │   └── n108042.jpg
    └── val
        ├── 561-n000127-miniature_pinscher
        │   ├── n161176.jpg
        │   ├── n161177.jpg
        │   ├── ...
        │   └── n161702.jpe
        ├── ...
        └── 806-n000129-papillon
            ├── n169982.jpg
            ├── n170022.jpg
            ├── ...
            └── n170736.jpeg

4.3 Train model

Train with proxy-nca loss

CUDA_VISIBLE_DEVICES=0 PYTHONPATH=./ python src/main.py --train_dir data/TsinghuaDogs/train --test_dir data/TsinghuaDogs/val --loss proxy_nca --config src/configs/proxy_nca_loss.yaml --checkpoint_root_dir src/checkpoints/proxynca-resnet50

Train with triplet loss

CUDA_VISIBLE_DEVICES=0 PYTHONPATH=./ python src/main.py --train_dir data/TsinghuaDogs/train --test_dir data/TsinghuaDogs/val --loss tripletloss --config src/configs/triplet_loss.yaml --checkpoint_root_dir src/checkpoints/tripletloss-resnet50

Run PYTHONPATH=./ python src/main.py --help for more detail about arguments.

If you want to train on 2 gpus, replace CUDA_VISIBLE_DEVICES=0 with CUDA_VISIBLE_DEVICES=0,1 and so on.

If you encounter out of memory issues, try reducing classes_per_batch and samples_per_class in src/configs/triplet_loss.yaml or batch_size in src/configs/your-loss.yaml

5. Evaluate

To evaluate, directory data should be structured like this:

data/
└── TsinghuaDogs
    ├── train
    │   ├── 561-n000127-miniature_pinscher
    │   │   ├── n107028.jpg
    │   │   ├── n107031.jpg
    │   │   ├── ...
    │   │   └── n107218.jp
    │   ├── ...
    │   ├── 806-n000129-papillon
    │   │   ├── n107440.jpg
    │   │   ├── n107451.jpg
    │   │   ├── ...
    │   │   └── n108042.jpg
    └── val
        ├── 561-n000127-miniature_pinscher
        │   ├── n161176.jpg
        │   ├── n161177.jpg
        │   ├── ...
        │   └── n161702.jpe
        ├── ...
        └── 806-n000129-papillon
            ├── n169982.jpg
            ├── n170022.jpg
            ├── ...
            └── n170736.jpeg

Plot confusion matrix

PYTHONPATH=./ python src/scripts/visualize_confusion_matrix.py --test_images_dir data/TshinghuaDogs/val/ --reference_images_dir data/TshinghuaDogs/train -c src/checkpoints/proxynca-resnet50.pth

Plot T-SNE

PYTHONPATH=./ python src/scripts/visualize_tsne.py --images_dir data/TshinghuaDogs/val/ -c src/checkpoints/proxynca-resnet50.pth

Plot similarity matrix

PYTHONPATH=./ python src/scripts/visualize_similarity.py  --images_dir data/TshinghuaDogs/val/ -c src/checkpoints/proxynca-resnet50.pth

6. Developement

.
├── __init__.py
├── README.md
├── src
│   ├── main.py  # Entry point for training.
│   ├── checkpoints  # Directory to save model's weights while training
│   ├── configs  # Configurations for each loss function
│   │   ├── proxy_nca_loss.yaml
│   │   └── triplet_loss.yaml
│   ├── dataset.py
│   ├── evaluate.py  # Calculate mean average precision, accuracy and NMI score
│   ├── __init__.py
│   ├── logs
│   ├── losses
│   │   ├── __init__.py
│   │   ├── proxy_nca_loss.py
│   │   └── triplet_margin_loss.py
│   ├── models  # Feature extraction models
│   │   ├── __init__.py
│   │   └── resnet.py
│   ├── samplers
│   │   ├── __init__.py
│   │   └── pk_sampler.py  # Sample triplets in each batch for triplet loss
│   ├── scripts
│   │   ├── __init__.py
│   │   ├── prepare_TsinghuaDogs.py  # download and prepare dataset for training and validating
│   │   ├── visualize_confusion_matrix.py
│   │   ├── visualize_similarity.py
│   │   └── visualize_tsne.py
│   ├── trainer.py  # Helper functions for training
│   └── utils.py  # Some utility functions
└── static
    ├── proxynca-resnet50
    │   ├── confusion_matrix.jpg
    │   ├── similarity.jpg
    │   ├── tsne_images.jpg
    │   └── tsne_points.jpg
    └── tripletloss-resnet50
        ├── confusion_matrix.jpg
        ├── similarity.jpg
        ├── tsne_images.jpg
        └── tsne_points.jpg

7. Acknowledgement

@article{Zou2020ThuDogs,
    title={A new dataset of dog breed images and a benchmark for fine-grained classification},
    author={Zou, Ding-Nan and Zhang, Song-Hai and Mu, Tai-Jiang and Zhang, Min},
    journal={Computational Visual Media},
    year={2020},
    url={https://doi.org/10.1007/s41095-020-0184-6}
}

Comments

Evaluate model - The system cannot find the path specified

@QuocThangNguyen Hello. I tried training and inference, they work fine (just training takes too long). Then I wanted to evaluate a trained model to see confusion matrix, T-SNE and similarity matrix, but I receive errors

FileNotFoundError: [WinError 3] The system cannot find the path specified: 'data/TshinghuaDogs/train' FileNotFoundError: [WinError 3] The system cannot find the path specified: 'data/TshinghuaDogs/val'

I followed the advice how to structure dataset for train and val folders, so I don't know why it can't find directories. Can you please help to figure out what is the issue and let me know if you need additional information from my side!

opened by HripsimeS 5
Tsinghua Dogs dataset preparation

@QuocThangNguyen Hello. I am having a bit problem in running prepare_TsinghuaDogs.py script for dataset preparation and getting error IndexError: list index out of range. Just wanted to check with you if I am doing things correctly.

So in data folder I opened TsinghuaDogs where I opened the following folders High-Annotations high-resolution TrainAndValList train val

Is this a correct data directory structure before running a prepare_TsinghuaDogs.py ?

opened by HripsimeS 2
checkpoints folder is empty

@QuocThangNguyen First of all, thanks for the great work on fine-grained classification. I would like to test the repository train and inference, but unfortunatly I could not find proxynca-resnet50.pth pre-trained model in checkpoints folder. Can you please let me know where I can find and dowload it. Look forward to hearing frolm you!

opened by HripsimeS 2
prepare_TsinghuaDogs not working

Hi,

I'm interested in using your dataset. However, when prepare_TsinghuaDogs.py is unzipping the downloaded files, an exception occurs:

Archive: data/TsinghuaDogs.zip End-of-central-directory signature not found. Either this file is not a zipfile, or it constitutes one disk of a multi-part archive. In the latter case the central directory and zipfile comment will be found on the last disk(s) of this archive. unzip: cannot find zipfile directory in one of data/TsinghuaDogs.zip or data/TsinghuaDogs.zip.zip, and cannot find data/TsinghuaDogs.zip.ZIP, period. 22-Jun-08 17:47:02 root INFO: Downloading https://cg.cs.tsinghua.edu.cn/ThuDogs/high-annotations.zip to data/TsinghuaDogs/high-annotations.zip

Could you please fix it or tell me the proper way to use it?

opened by Mo-Kanya 2
How can I convert .pth weight to tflite?

@QuocThangNguyen Hello!

I would like to convert a pytorch (pth) trained model to TensorFlow Lite (tflite) format. Do you have some advice how to do it properly for your Fine-Grained classification models? I saw that in general the converting process has several steps, convert Pytorch to ONNX, ONNX to TensorFlow and then TensorFlow to TensorFlow Lite. But if you know a quick way to do it or maybe there is a special script that can do all these steps without errors, that would be great.

Look forward to hearing from you!

opened by HripsimeS 1
example code for pretrained model

Dear QuocThangNguyen,

Thank you for your great work about Tsinghua Dogs dataset, I really like it.

In the main pape, you provide pretrained model, would you mind provide the usuage codes for the pretrained model? Did you do any preprocessing before fitting the image to the model?

You mention about other benchmarks in project main page, but there is not pretrained model, so I do not know the pretrained model in github page is using which method.

Thank you for your help.

Best Wishes,

Alex

opened by betterze 1
output category

Dear QuocThangNguyen,

Thank you for your great work about Tsinghua Dogs dataset, I really like it.

In the main pape, you provide pretrained model, this model only provide the image embedding for retrieval rather than output the dog category. Is there a way to get the dog category given an image?

Thank you for your help.

Best Wishes,

Alex
question

opened by betterze 1