Update README.md

README.md

@@ -49,7 +49,7 @@ dataset_summary: '
 
   # Note: other available arguments include ''max_samples'', etc
 
-  dataset = fouh.load_from_hub("jamarks/Stanford-Dogs-Imbalanced")
+  dataset = fouh.load_from_hub("Voxel51/Stanford-Dogs-Imbalanced")
 
 
   # Launch the App
@@ -89,7 +89,7 @@ import fiftyone.utils.huggingface as fouh
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = fouh.load_from_hub("jamarks/Stanford-Dogs-Imbalanced")
+dataset = fouh.load_from_hub("Voxel51/Stanford-Dogs-Imbalanced")
 
 # Launch the App
 session = fo.launch_app(dataset)
@@ -100,8 +100,28 @@ session = fo.launch_app(dataset)
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
+An imbalanced version of the [Stanford Dogs dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) designed for testing class imbalance mitigation techniques, including but not limited to synthetic data generation.
 
+This version of the dataset was constructed by randomly splitting the original dataset into train, val, and test sets with a 60/20/20 split. For 15 randomly chosen classes, we then removed all but 10 of the training examples.
+
+```python
+# Split the dataset into train, val, and test sets
+import fiftyone.utils.random as four
+train, val, test = four.random_split(dataset, split_fracs=(0.6, 0.2, 0.2))
+splits_dict = { "train": train, "val": val, "test": test }
+
+# Get the classes to limit
+import random
+classes = list(dataset.distinct("ground_truth.label"))
+classes_to_limit = random.sample(classes, 15)
+
+# Limit the number of samples for the selected classes
+for class_name in classes_to_limit:
+    class_samples = dataset.match(F("ground_truth.label") == class_name)
+    samples_to_keep = class_samples.take(10)
+    samples_to_remove = class_samples.exclude(samples_to_keep)
+    dataset.delete_samples(samples_to_remove)
+```
 
 
 - **Curated by:** [More Information Needed]
@@ -110,120 +130,66 @@ session = fo.launch_app(dataset)
 - **Language(s) (NLP):** en
 - **License:** [More Information Needed]
 
-### Dataset Sources [optional]
+### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Paper:** [More Information Needed]
+- **Homepage:** [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. -->
+- Fine-grained visual classification
+- Class imbalance mitigation strategies
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+<!-- Address questions around how the dataset is intended to be used. -->
 
-[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]
+The following classes only have 10 samples in the train split:
+
+- Australian_terrier
+- Saluki
+- Cardigan
+- standard_schnauzer
+- Eskimo_dog
+- American_Staffordshire_terrier
+- Lakeland_terrier
+- Lhasa
+- cocker_spaniel
+- Greater_Swiss_Mountain_dog
+- basenji
+- toy_terrier
+- Chihuahua
+- Walker_hound
+- Shih-Tzu
+- Newfoundland
 
-<!-- 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.
+<!-- 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. -->
 
-## Citation [optional]
 
+## Citation
 <!-- 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]
+```bibtex
+@inproceedings{KhoslaYaoJayadevaprakashFeiFei_FGVC2011,
+  author = "Aditya Khosla and Nityananda Jayadevaprakash and Bangpeng Yao and Li Fei-Fei",
+  title = "Novel Dataset for Fine-Grained Image Categorization",
+  booktitle = "First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition",
+  2011,
+  month = "June",
+  address = "Colorado Springs, CO",
+}
+```
 
-[More Information Needed]
 
-## Dataset Card Authors [optional]
+## Dataset Card Author
 
-[More Information Needed]
+[Jacob Marks](https://huggingface.co/jamarks)
 
-## Dataset Card Contact
+## Dataset Contacts
 
-[More Information Needed]
+aditya86@cs.stanford.edu and bangpeng@cs.stanford.edu