Datasets:

Voxel51
/

Food101

@@ -6,23 +6,24 @@ size_categories:
 task_categories:
 - image-classification
 task_ids: []
-pretty_name: 2024.07.05.21.39.46
 tags:
 - fiftyone
 - image
 - image-classification
-dataset_summary: '
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
   ## Installation
-  If you haven''t already, install FiftyOne:
   ```bash
@@ -44,9 +45,9 @@ dataset_summary: '
   # Load the dataset
-  # Note: other available arguments include ''max_samples'', etc
-  dataset = fouh.load_from_hub("harpreetsahota/Food101")
   # Launch the App
@@ -54,20 +55,17 @@ dataset_summary: '
   session = fo.launch_app(dataset)
   ```
-  '
 ---
-# Dataset Card for 2024.07.05.21.39.46
-<!-- Provide a quick summary of the dataset. -->
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
 ## Installation
 If you haven't already, install FiftyOne:
@@ -84,7 +82,7 @@ import fiftyone.utils.huggingface as fouh
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = fouh.load_from_hub("harpreetsahota/Food101")
 # Launch the App
 session = fo.launch_app(dataset)
@@ -95,130 +93,35 @@ session = fo.launch_app(dataset)
 ### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- 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. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- 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. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- 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. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
-## Dataset Card Contact
-[More Information Needed]

 task_categories:
 - image-classification
 task_ids: []
+pretty_name: Food101
 tags:
 - fiftyone
 - image
 - image-classification
+dataset_summary: >
+  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000
+  samples.
   ## Installation
+  If you haven't already, install FiftyOne:
   ```bash
   # Load the dataset
+  # Note: other available arguments include 'max_samples', etc
+  dataset = fouh.load_from_hub("Voxel51/Food101")
   # Launch the App
   session = fo.launch_app(dataset)
   ```
 ---
+# Dataset Card for Food-101
+[!image](food-101.gif)
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 35000 samples.
+**Note:** This dataset is subset of the full Food101 dataset. The recipe notebook for creating this dataset can be found [here](https://colab.research.google.com/drive/11ZDZxaRTVR3DjANNR4p5CnCYqlTYmpfT)
 ## Installation
 If you haven't already, install FiftyOne:
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
+dataset = fouh.load_from_hub("Voxel51/Food101")
 # Launch the App
 session = fo.launch_app(dataset)
 ### Dataset Description
+The Food-101 dataset is a large-scale dataset for food recognition, consisting of 101,000 images across 101 different food categories.
+Here are the key details:
+- Contains a total of 101,000 images
+- Each food class has 1,000 images, with 750 training images and 250 test images per class
+- All images were rescaled to have a maximum side length of 512 pixels
+- **Curated by:** Lukas Bossard, Matthieu Guillaumin, Luc Van Gool
+- **Funded by:** Computer Vision Lab, ETH Zurich, Switzerland
+- **Shared by:** [Harpreet Sahota](twitter.com/datascienceharp), Hacker-in-Residence at Voxel51
 - **Language(s) (NLP):** en
+- **License:** The dataset images come from Foodspotting and are not owned by the creators of the Food-101 dataset (ETH Zurich). Any use beyond scientific fair use must be negotiated with the respective picture owners according to the Foodspotting terms of use
+### Dataset Sources
+- **Repository:** https://huggingface.co/datasets/ethz/food101
+- **Website:** https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/
+- **Paper:** https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf
+## Citation
 **BibTeX:**
+```bibtex
+@inproceedings{bossard14,
+  title = {Food-101 -- Mining Discriminative Components with Random Forests},
+  author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
+  booktitle = {European Conference on Computer Vision},
+  year = {2014}
+}
+```