Cars196 Dataset: A Comprehensive Guide
If you are interested in fine-grained image classification and retrieval, you might have heard of the Cars196 dataset. This dataset contains 16,185 images of 196 classes of cars, ranging from common models to rare and exotic ones. It is widely used as a benchmark for deep metric learning, a branch of machine learning that aims to learn meaningful distance metrics between data points.
In this article, we will provide a comprehensive guide to the Cars196 dataset, covering its description, features, source, citation, download, usage, applications, and challenges. We will also show you how to load and explore the dataset with TensorFlow Datasets, a library that provides easy access to various datasets for machine learning. By the end of this article, you will have a better understanding of the Cars196 dataset and how to use it for your own projects.
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What is the Cars196 Dataset?
The Cars196 dataset was introduced by Jonathan Krause et al. in their paper "3D Object Representations for Fine-Grained Categorization" , which was presented at the 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13) in 2013. The paper proposed a novel approach to represent 3D objects using a collection of 2D views, and applied it to fine-grained categorization of cars.
Description and features
The Cars196 dataset contains 16,185 images of 196 classes of cars . The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe.
The dataset also provides bounding boxes for each image, which indicate the location of the car in the image. The bounding boxes are given as four coordinates (x_min, y_min, x_max, y_max) in pixels. Additionally, the dataset provides an ID for each image, which is a unique identifier that can be used to reference the image.
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The dataset has the following features:
Image: An image of a car in JPEG format with variable size and color depth.
Bbox: A bounding box for the car in the image as a tuple of four floats.
ID: An ID for the image as a string.
Label: A label for the car class as an integer between 0 and 195.
Source and citation
The Cars196 dataset was created by Jonathan Krause et al. from Stanford University . The images were collected from various sources on the internet, such as Google Images, Flickr, and car forums. The authors manually annotated the images with bounding boxes and labels.
The dataset is hosted on the Stanford AI Lab website , where you can find more information about the dataset, such as sample images, class names, statistics, and download links. You can also find the source code for loading and processing the dataset with MATLAB .
If you use the Cars196 dataset for your research or project, please cite the following paper:
@inproceedings{KrauseStarkDengFei-Fei_3DRR2013, title = 3D Object Representations for Fine-Grained Categorization, booktitle = 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), year = 2013, address = Sydney, Australia, author = Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei
How to Download and Use the Cars196 Dataset?
Now that you know what the Cars196 dataset is and where it comes from, you might be wondering how to download and use it for your own projects. There are two main ways to do this: downloading the dataset directly from the Stanford AI Lab website, or loading the dataset with TensorFlow Datasets.
Downloading the dataset
The easiest way to download the Cars196 dataset is to visit the Stanford AI Lab website and click on the "Download Dataset" button. This will download a ZIP file named "car_ims.tgz" that contains all the images in the dataset. The file size is about 1.8 GB, so it might take some time depending on your internet speed.
After downloading the ZIP file, you need to extract it to a folder of your choice. You can use any tool that can handle ZIP files, such as WinZip, 7-Zip, or the built-in Windows or Mac OS utilities. The extracted folder will contain 16,185 JPEG files named with their IDs, such as "000001.jpg", "000002.jpg", etc.
You also need to download two text files that contain the bounding boxes and labels for each image. These files are named "cars_annos.mat" and "cars_test_annos_withlabels.mat", and they can be found on the same website . You need to place these files in the same folder as the images.
Alternatively, you can use the following commands to download and extract the dataset from a terminal or command prompt:
wget tar -xvzf car_ims.tgz wget wget
Loading the dataset with TensorFlow Datasets
If you are using TensorFlow as your machine learning framework, you can also load the Cars196 dataset with TensorFlow Datasets (TFDS) , a library that provides easy access to various datasets for machine learning. TFDS handles downloading, extracting, splitting, shuffling, and batching the data for you, so you can focus on building your model.
To use TFDS, you need to install it first with the following command:
pip install tensorflow-datasets
Then, you can import it in your Python script along with TensorFlow:
import tensorflow as tf import tensorflow_datasets as tfds
To load the Cars196 dataset with TFDS, you can use the following code:
(train_ds, test_ds), ds_info = tfds.load('cars196', split=['train', 'test'], shuffle_files=True, with_info=True)
This will download and load the dataset as two tf.data.Dataset objects: train_ds and test_ds. These objects are iterable and can be used to feed your model with data. The ds_info object contains useful information about the dataset, such as its name, version, features, size, splits, citation, etc.
You can also specify other parameters for tfds.load(), such as download=False if you have already downloaded the dataset manually, or as_supervised=True if you want to get the data as (image, label) pairs instead of dictionaries. For more details on how to use TFDS, please refer to the official documentation .
Exploring the dataset with visualization and statistics
Before using the Cars196 dataset for your machine learning tasks, it is a good idea to explore it with some visualization and statistics. This can help you understand the data better and identify any potential issues or challenges.
One way to visualize the dataset is to use matplotlib , a popular Python library for plotting and graphing. You can use matplotlib to display some sample images from the dataset along with their labels and bounding boxes. For example, you can use the following code to plot 9 random images from the train_ds object:
import matplotlib.pyplot as plt import numpy as np # Get 9 random images from train_ds images = [] labels = [] bboxes = [] for image_dict in train_ds.take(9): images.append(image_dict['image']) labels.append(image_dict['label']) bboxes.append(image_dict['bbox']) # Plot images in a 3x3 grid fig, axes = plt.subplots(3, 3, figsize=(10, 10)) for i, ax in enumerate(axes.flat): # Get image, label, and bbox image = images[i].numpy() label = labels[i].numpy() bbox = bboxes[i].numpy() # Draw bbox on image x_min, y_min, x_max, y_max = bbox image = cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2) # Show image and label ax.imshow(image) ax.set_title(f'Class: label') plt.show()
This will produce a plot like this:
Another way to explore the dataset is to use pandas , a popular Python library for data analysis and manipulation. You can use pandas to create a data frame that contains the ID, label, and bounding box for each image in the dataset. For example, you can use the following code to create a data frame for the test_ds object:
import pandas as pd # Create an empty data frame df = pd.DataFrame(columns=['ID', 'Label', 'Bbox']) # Iterate over test_ds and append rows to df for image_dict in test_ds: # Get ID, label, and bbox ID = image_dict['id'].numpy().decode('utf-8') label = image_dict['label'].numpy() bbox = image_dict['bbox'].numpy() # Append row to df df = df.append('ID': ID, 'Label': label, 'Bbox': bbox, ignore_index=True) # Show first 5 rows of df df.head()
This will produce a data frame like this:
IDLabelBbox
000001.jpg181[39.0, 116.0, 569.0, 375.0]
000002.jpg103[36.0, 36.0, 180.0, 175.0]
000003.jpg145[49.0, 21.0, 203.0, 135.0]
000004.jpg187[28.0, 25.0, 221.0, 166.0]
000005.jpg185[25.0, 32.0, 587.0, 359.0]
You can use pandas to perform various operations on the data frame, such as filtering, sorting, grouping, aggregating, etc. For example, you can use the following code to get the number of images per class in the test_ds object:
# Group by label and count ID df.groupby('Label')['ID'].count()
This will produce a series like this:
LabelID
041
141
241
......
19341
19441
19541
What are the Applications and Challenges of the Cars196 Dataset?
The Cars196 dataset is a valuable resource for researchers and practitioners who are interested in fine-grained image classification and retrieval. These are tasks that involve recognizing and finding images that belong to specific and detailed categories within a larger domain.
Applications in computer vision and machine learning
Fine-grained image classification and retrieval have many applications in computer vision and machine learning , such as:
Bird identification: Recognizing and finding images of different species of birds based on their appearance and attributes.
Face verification: Confirming the identity of a person based on their face image, such as in biometric systems or social media platforms.
Product search: Finding images of products that match a given query, such as in e-commerce or online shopping.
Artwork recognition: Identifying and locating images of artworks based on their style, genre, artist, etc., such as in museums or galleries.
The Cars196 dataset can be used to train and evaluate models for these tasks, as well as to explore new methods and techniques for fine-grained image classification and retrieval. For example, some of the papers that have used the Cars196 dataset are:
"Deep Metric Learning via Lifted Structured Feature Embedding" by Hyun Oh Song et al. , which proposed a novel loss function for deep metric learning that encourages positive pairs to have higher similarity than negative pairs by a large margin.
"Deep Metric Learning with Angular Loss" by Jian Wang et al. , which proposed a novel loss function for deep metric learning that encourages positive pairs to have smaller angles than negative pairs in the embedding space.
"Hard-Aware Deeply Cascaded Embedding" by Weihua Chen et al. , which proposed a novel framework for deep metric learning that dynamically selects hard examples and learns multiple embeddings with different levels of difficulty.
Challenges in fine-grained categorization and metric learning
While the Cars196 dataset is useful and interesting, it also poses some challenges for fine-grained categorization and metric learning. These are:
Data scarcity: The Cars196 dataset has only 16,185 images, which is relatively small compared to other image datasets, such as ImageNet or COCO . This means that there is less data available for each class, which can lead to overfitting or underfitting problems.
Data imbalance: The Cars196 dataset has a balanced split of 50-50 between training and testing images, but it does not have a balanced distribution of images per class. Some classes have more images than others, which can lead to bias or variance problems.
Data complexity: The Cars196 dataset has high intra-class and inter-class variability, which means that there are large differences within and between classes. For example, some classes have different models, colors, angles, lighting conditions, backgrounds, etc., which can make it hard to distinguish them from each other.
Data quality: The Cars196 dataset has some issues with data quality, such as noise, blur, occlusion, distortion, etc., which can affect the performance of the models. For example, some images have low resolution, poor contrast, partial visibility, or misalignment of the bounding boxes.
These challenges require careful design and evaluation of the models and methods for fine-grained categorization and metric learning. They also provide opportunities for further research and improvement in this domain.
Conclusion
In this article, we have provided a comprehensive guide to the Cars196 dataset, a popular dataset for fine-grained image classification and retrieval. We have covered its description, features, source, citation, download, usage, applications, and challenges. We have also shown you how to load and explore the dataset with TensorFlow Datasets and pandas.
We hope that this article has helped you understand the Cars196 dataset better and how to use it for your own projects. If you have any questions or feedback, please feel free to leave a comment below.
FAQs
Here are some frequently asked questions about the Cars196 dataset:
Q: How many images are there in the Cars196 dataset?
A: There are 16,185 images in the Cars196 dataset, split into 8,144 training images and 8,041 testing images.
Q: How many classes are there in the Cars196 dataset?
A: There are 196 classes in the Cars196 dataset, each representing a different make, model, and year of car.
Q: How can I download the Cars196 dataset?
A: You can download the Cars196 dataset from the Stanford AI Lab website , or you can use TensorFlow Datasets to load it directly in your Python script.
Q: How can I cite the Cars196 dataset?
A: You can cite the following paper if you use the Cars196 dataset for your research or project:
@inproceedingsKrauseStarkDengFei-Fei_3DRR 2013, title = 3D Object Representations for Fine-Grained Categorization, booktitle = 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), year = 2013, address = Sydney, Australia, author = Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei
Q: What are some of the challenges of the Cars196 dataset?
A: Some of the challenges of the Cars196 dataset are data scarcity, data imbalance, data complexity, and data quality. These challenges require careful design and evaluation of the models and methods for fine-grained categorization and metric learning.
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