Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
Update README.md
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README.md
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# Note: other available arguments include ''max_samples'', etc
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dataset = fouh.load_from_hub("
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# Launch the App
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# Dataset Card for ThermalPersonDetector
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### Dataset Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** en
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- **License:** [More Information Needed]
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### Dataset Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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<!-- This section describes suitable use cases for the dataset. -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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[More Information Needed]
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## Dataset Structure
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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<!-- Motivation for the creation of this dataset. -->
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[More Information Needed]
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### Source Data
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#### Data Collection and Processing
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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[More Information Needed]
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#### Who are the source data producers?
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[More Information Needed]
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### Annotations [optional]
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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#### Annotation process
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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#### Personal and Sensitive Information
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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## Dataset Card Authors [optional]
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[More Information Needed]
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## Dataset Card Contact
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[More Information Needed]
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# Note: other available arguments include ''max_samples'', etc
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dataset = fouh.load_from_hub("Voxel51/Thermal-Person-Detector")
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# Launch the App
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# Dataset Card for ThermalPersonDetector
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A thermal image dataset for detecting people in a scene. The dataset contains only one class `person`
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### Dataset Description
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Here are a few use cases for this project:
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Sports Analytics: The "PersonDetection" model could be used to analyze individual athletes' performances in various sports such as skateboarding, basketball, or soccer, by detecting and tracking the movements of players.
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Surveillance Security: It could be utilized in CCTV systems and security cameras. By recognizing people in real-time, it could alert security personnel when unauthorized individuals are detected in restricted areas.
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Social Distancing Detection: In light of the Covid-19 pandemic, this model could be used to enforce social distancing measures by tracking the number of people and their relative distances in public spaces like parks, malls, or transportation systems.
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Smart Home Management: It can be deployed in smart homes devices to recognize the home occupants and subsequently adapt the environment to their preferences such as lighting, temperature or even play their favorite music upon entry.
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Motion Capture and Gaming: In the gaming and animation industry, this model could be used for real-time motion capture, allowing developers to create more realistic and immersive human characters.
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- **Language(s) (NLP):** en
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- **License:** CC 4.0
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## Dataset Structure
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One ground_truth field with a person class
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[More Information Needed]
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### Source Data
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[Roboflow Univers](https://universe.roboflow.com/smart2/persondection-61bc2)
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#### Who are the source data producers?
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[SMART2](https://universe.roboflow.com/smart2)
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[More Information Needed]
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#### Who are the annotators?
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SMART2
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**BibTeX:**
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@misc{
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persondection-61bc2_dataset,
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title = { PersonDection Dataset },
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type = { Open Source Dataset },
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author = { SMART2 },
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howpublished = { \url{ https://universe.roboflow.com/smart2/persondection-61bc2 } },
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url = { https://universe.roboflow.com/smart2/persondection-61bc2 },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2023 },
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month = { may },
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note = { visited on 2024-07-19 },
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}
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