Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
ArXiv:
harpreetsahota
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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 lecture_dataset_train
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 16638 samples.
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# Load the dataset
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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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session = fo.launch_app(dataset)
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### Dataset Description
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:**
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- **Paper
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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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## Dataset Creation
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### Curation Rationale
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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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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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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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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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[More Information Needed]
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## More Information [optional]
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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/Coursera_lecture_dataset_train")
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# Launch the App
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# Dataset Card for lecture_dataset_train
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This dataset is the **training dataset for the in-class lectures** of the Hands-on Data Centric AI Coursera course.
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 16638 samples.
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# Load the dataset
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# Note: other available arguments include 'max_samples', etc
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dataset = fouh.load_from_hub("Voxel51/Coursera_lecture_dataset_train")
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# Launch the App
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session = fo.launch_app(dataset)
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### Dataset Description
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This dataset is a modified subset of the [LVIS dataset](https://www.lvisdataset.org/).
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The dataset here only contains detections, some of which have been artificially perturbed and altered to demonstrate data centric AI techniques and methodologies for the course.
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This dataset has the following labels:
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- 'jacket'
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- 'coat'
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- 'jean'
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- 'trousers'
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- 'short_pants'
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- 'trash_can'
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- 'bucket'
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- 'flowerpot'
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- 'helmet'
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- 'baseball_cap'
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- 'hat'
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- 'sunglasses'
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- 'goggles'
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- 'doughnut'
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- 'pastry'
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- 'onion'
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- 'tomato'
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:** https://www.lvisdataset.org/
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- **Paper:** https://arxiv.org/abs/1908.03195
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## Uses
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The labels in this dataset have been perturbed to illustrate data centric AI techniques for the Hands-on Data Centric AI Coursera MOOC.
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## Dataset Structure
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Each image in the dataset comes with detailed annotations in FiftyOne detection format. A typical annotation looks like this:
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```python
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<Detection: {
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'id': '66a2f24cce2f9d11d98d39f3',
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'attributes': {},
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'tags': [],
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'label': 'trousers',
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'bounding_box': [
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0.5562343750000001,
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0.4614166666666667,
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0.1974375,
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0.29300000000000004,
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],
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'mask': None,
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'confidence': None,
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'index': None,
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}>
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```
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## Dataset Creation
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### Curation Rationale
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The selected labels for this dataset is due to the fact that these objects can be confusing to a model. Thus, making them a great choice for demonstrating data centric AI techniques.
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[More Information Needed]
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### Source Data
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This is a subset of the [LVIS dataset.](https://www.lvisdataset.org/)
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## Citation
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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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```bibtex
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@inproceedings{gupta2019lvis,
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title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
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author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
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booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
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year={2019}
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}
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```
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