Datasets:

Voxel51
/

American-Sign-Language-MNIST

@@ -1,226 +1,160 @@
 ---
-annotations_creators: []
-language: en
 license: mit
-size_categories:
-- 10K<n<100K
 task_categories:
 - image-classification
-task_ids: []
 pretty_name: american-sign-language-mnist
-tags:
-- fiftyone
-- image
-- image-classification
-repo_type: dataset
-dataset_summary: '
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 34627 samples.
-  ## Installation
-  If you haven''t already, install FiftyOne:
-  ```bash
-  pip install -U fiftyone
-  ```
-  ## Usage
-  ```python
-  import fiftyone as fo
-  from fiftyone.utils.huggingface import load_from_hub
-  # Load the dataset
-  # Note: other available arguments include ''max_samples'', etc
-  dataset = load_from_hub("andandandand/American-Sign-Language-MNIST")
-  # Launch the App
-  session = fo.launch_app(dataset)
-  ```
-  '
 ---
-# Dataset Card for american-sign-language-mnist
-<!-- Provide a quick summary of the dataset. -->
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 34627 samples.
-## Installation
-If you haven't already, install FiftyOne:
-```bash
-pip install -U fiftyone
-```
-## Usage
-```python
-import fiftyone as fo
-from fiftyone.utils.huggingface import load_from_hub
-# Load the dataset
-# Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("andandandand/American-Sign-Language-MNIST")
-# Launch the App
-session = fo.launch_app(dataset)
-```
 ## Dataset Details
-### 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:** mit
-### 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]

 ---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+        - A
+        - B
+        - C
+        - D
+        - E
+        - F
+        - G
+        - H
+        - I
+        - K
+        - L
+        - M
+        - 'N'
+        - O
+        - P
+        - Q
+        - R
+        - S
+        - T
+        - U
+        - V
+        - W
+        - X
+        - 'Y'
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 0
+  - name: test
+    num_bytes: 0
+    num_examples: 0
+  download_size: 0
+  dataset_size: 0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train/*
+  - split: test
+    path: test/*
 license: mit
+tags:
+- computer-vision
+- image-classification
+- sign-language
+- asl
+- mnist
+- gesture-recognition
 task_categories:
 - image-classification
+task_ids:
+- multi-class-image-classification
+paperswithcode_id: asl-mnist
+size_categories:
+- 10K<n<100K
+language:
+- en
+multilinguality:
+- monolingual
+source_datasets:
+- original
+annotations_creators:
+- expert-generated
 pretty_name: american-sign-language-mnist
 ---
+# ASL-MNIST Dataset Card
+## Dataset Description
+The American Sign Language MNIST dataset contains static hand gesture images representing letters of the ASL alphabet. This dataset provides image classification data for training models to recognize ASL hand poses.
 ## Dataset Details
+- **Total samples**: Varies by split (training and test sets available)
+- **Image format**: JPG
+- **Number of classes**: 24 (out of 26 alphabet letters)
+- **Image type**: Grayscale hand gesture photographs
+- **Split**: Training and test sets provided
+## Classes
+The dataset includes 24 ASL letters:
+A, B, C, D, E, F, G, H, I, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y
+### Missing Letters
+J (index 9) and Z (index 25) are excluded because they require hand motion in ASL. Static images cannot capture these motion-based letters.
 ## Dataset Structure
+```
+asl-mnist/
+├── train/
+│   ├── train_0.jpg
+│   ├── train_1.jpg
+│   └── ...
+├── test/
+│   ├── test_0.jpg
+│   ├── test_1.jpg
+│   └── ...
+├── train.csv
+└── test.csv
+```
+## Label Mapping
+The dataset uses numeric indices that map to letters:
+```python
+asl_labels = {
+    0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I',
+    10: 'K', 11: 'L', 12: 'M', 13: 'N', 14: 'O', 15: 'P', 16: 'Q', 17: 'R',
+    18: 'S', 19: 'T', 20: 'U', 21: 'V', 22: 'W', 23: 'X', 24: 'Y'
+}
+```
+## Data Fields
+Each sample contains:
+- `image`: The hand gesture image file
+- `label`: Classification label (letter A-Y, excluding J and Z)
+- `split`: "train" or "test"
+## Usage
+This dataset is suitable for:
+- Image classification tasks
+- Computer vision model training
+- ASL recognition research
+- Educational projects on sign language
+## Limitations
+- Contains only static poses (no motion-based letters)
+- Limited to 24 out of 26 alphabet letters
+- Single hand gestures only
+- Grayscale images may limit color-based feature learning
+## Citation
+If you use this dataset in your research, please cite the original ASL-MNIST dataset, available through Kaggle
+https://www.kaggle.com/datasets/datamunge/sign-language-mnist/data
+## Technical Implementation
+The dataset has been processed and formatted for use with FiftyOne, a computer vision dataset management tool. The implementation includes:
+- Automatic label mapping from numeric indices to letters
+- Train/test split preservation
+- Metadata computation for efficient querying
+- Support for filtering unknown labels