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MARS Dataset

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Visualization of the MARS dataset in the Deep Lake UI

MARS dataset

What is MARS Dataset?

The MARS (Motion Analysis and Re-distinguishing proof Set) is a large video-based individual reidentification dataset. MARS is an extension of the Market-1501 dataset. The dataset was created by placing six near-synchronized cameras (one of which was a 640*480 SD camera, and the other five cameras were 1,080*1920 HD cameras) on the campus of Tsinghua University. The MARS dataset is made up of 1,261 unique pedestrians that were captured by two or more cameras.

Download MARS Dataset in Python

Instead of downloading the MARS dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load MARS Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-mars-train")

Load MARS Dataset Test Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/emnist-mars-test")

MARS Dataset Structure

MARS Data Fields
  • images: tensor to represent an image in jpg format.
  •  video_nos: tensor to identify the video to which frame belongs.
  • frame_nos: tensor to identify each frame within a video.
  • camera_nos: tensor to identify the camera id.
  • pedestrian_ids: tensor to identify the person with a unique id.
  • track_nos: tensor to uniquely identify the track.
MARS Data Splits

How to use MARS Dataset with PyTorch and TensorFlow in Python

Train a model on MARS dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on the MARS dataset with TensorFlow in Python
				
					dataloader = ds.tensorflow()

Additional Information about MARS Dataset

MARS Dataset Description

  • Homepage: http://zheng-lab.cecs.anu.edu.au/Project/project_mars.html
  • Repository: N/A
  • Paper: Introduced by Zheng, Liang and Bie, Zhi and Sun, Yifan and Wang, Jingdong and Su, Chi and Wang, Shengjin and Tian, Qi in MARS: A Video Benchmark for Large-Scale Person Re-identification
  • Point of Contact: N/A
MARS Dataset Curators

X Zheng, Liang and Bie, Zhi and Sun, Yifan and Wang, Jingdong and Su, Chi and Wang, Shengjin and Tian, Qi

MARS Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

MARS Dataset Citation Information
				
					@proceedings{zheng2016mars,
  title={MARS: A Video Benchmark for Large-Scale Person Re-identification},
  author={Zheng, Liang and Bie, Zhi and Sun, Yifan and Wang, Jingdong and Su, Chi and Wang, Shengjin and Tian, Qi},
  booktitle={European Conference on Computer Vision},
  year={2016},
  organization={Springer}
}

MARS Dataset FAQs

What is the MARS dataset for Python?

MARS (Motion Analysis and Re-identification Set) is a large-scale video-based person reidentification dataset that is a follow-up to the Market-1501 dataset. In addition, the dataset includes 3,248 distractors to make it more realistic. The tracklets were generated automatically using the Deformable Part Model and the GMMCP tracker (mostly 25-50 frames long).

How to download the MARS dataset in Python?

You can load the MARS dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load the MARS dataset training subset and testing subset in Python.

How can I use the MARS dataset in PyTorch or TensorFlow?

You can stream the MARS dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on the Mars dataset with PyTorch or train a model on the MARS dataset with TensorFlow.

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