Update README.md

README.md

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----
-license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: original
-    path: data/original-*
-  - split: augmented
-    path: data/augmented-*
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': class_0
-          '1': class_1
-  splits:
-  - name: original
-    num_bytes: 428094611.0
-    num_examples: 35
-  - name: augmented
-    num_bytes: 30021154.0
-    num_examples: 385
-  download_size: 458137914
-  dataset_size: 458115765.0
----
+---
+license: apache-2.0
+configs:
+- config_name: default
+  data_files:
+  - split: original
+    path: data/original-*
+  - split: augmented
+    path: data/augmented-*
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': class_0
+          '1': class_1
+  splits:
+  - name: original
+    num_bytes: 428094611
+    num_examples: 35
+  - name: augmented
+    num_bytes: 30021154
+    num_examples: 385
+  download_size: 458137914
+  dataset_size: 458115765
+language:
+- en
+pretty_name: a
+---
+
+# Dataset Card for Dataset Name
+
+<!-- Provide a quick summary of the dataset. -->
+This dataset is designed for binary image classification of stop signs. It contains two splits: 'original' with 35 images and 'augmented' with 385 images. The augmented split was created by applying various image transformations to the original images to increase the dataset size and diversity for model training.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+This dataset contains images of signs with binary labels (class_0 == image has stop sign and class_1 == image doesn't have a stop sign). The 'original' split contains the raw images, while the 'augmented' split contains augmented versions of the images generated using various transformations like random resized crop, horizontal/vertical flips, rotation, color jitter, sharpness adjustment, and autocontrast.
+
+
+- **Curated by:** Emily Copus
+- **Shared by:** @ecopus (Hugging Face Hub)
+- **Language(s) (NLP):** English
+- **License:** apache-2.0
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://huggingface.co/datasets/ecopus/sign_identification
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+This dataset can be used for training and evaluating image classification models for identifying the two classes of signs. The augmented split can be particularly useful for improving model generalization and robustness, especially with a limited number of original examples.
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+This is a limited dataset, with a single, binary target assignment per image. Ultimately, this dataset is not suited to contribute to ML algorithms outside of the identification of specifically stop signs. 
+The user should refrain from utilizing models trained with this data for sensitive stop sign identification applications (i.e., self-driving applications).
+
+
+## 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. -->
+The dataset has two splits: 'original' and 'augmented'. Each split contains two features:
+- image: A datasets.Image object representing the image.
+- label: A datasets.ClassLabel object with two classes: 'class_0' and 'class_1'.
+Class_0 refers to all images in which a stop sign is not present - conversely, class_1 refers to all images in which a stop sign is present. 
+The 'original' split contains 35 examples and the 'augmented' split contains 385 examples.
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+This dataset was curated as a basic learning tool for implementing ML tools with image datasets. The simplicity of this dataset allows for easy implementation into basic ML binary classification algorithms, pefect for a first time user.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+The images in this dataset were manually captured by the curator from non-disclosed locations.
+
+#### 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. -->
+Again, this data was collected and compiled manually. The criterion for an acceptable image was:
+(a) contains some road sign
+(b) the full sign can be seen in the image
+(c) any text on the sign can be distinguished by the naked eye
+Class_1 (stop sign is in the image) consists of about 50% of the original split.
+
+#### 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. -->
+This data was produced entirely by the curator.
+
+#### 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. -->
+Labels (1 or 0) were manually assigned to each image at their respective indices. 
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+This dataset was annnotated by the curator. 
+
+#### 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. -->
+This dataset contains no personal or sensitive information.
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Technical Limitations
+- Small Original Dataset Size: The original dataset only contains 35 images, which is a very small number for training a robust image classification model. While data augmentation helps increase the number of examples, it doesn't introduce truly new information or diversity.
+- Binary Classification: The dataset is limited to a binary classification problem (two classes). It cannot be used directly for multi-class sign identification without further annotation.
+- HEIC Image Handling: The dataset was created by processing HEIC images. While the conversion to PNG was successful, relying on specific image formats and conversion processes can introduce potential issues or dependencies.
+- Limited Augmentation Techniques: While several augmentation techniques were used, exploring a wider range of augmentations or more advanced techniques might be necessary for more sensitive applications.
+Sociotechnical Limitations
+- Lack of Diversity: The dataset might lack diversity in terms of variations in lighting conditions, angles, distances, occlusions, or different styles of the same sign, which could impact the model's performance in real-world scenarios. Most importantly, this dataset does not collect images across varying locations or weather conditions, making it unsuitable to be applied broadly.
+- Ethical Considerations: Depending on the specific types of signs and their context, there could be ethical implications in developing a sign identification system, such as privacy concerns or the potential for misuse. Though this was carefully mitigated upon initial creation of this dataset, there are still possible safety implications of the widespread availability of these images. 
+- Generalizability: Due to the small size and potential biases, the dataset is unlikely to be representative of a broad population of signs, limiting the generalizability of models trained on it.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should be aware of the risks, biases and limitations of the dataset before use (see above). Refrain from utilizing this dataset for applications outside of algorithm creation optimization/ education. This dataset should not be utilized to draw real-world conclusions.
+
+## Dataset Card Authors
+Emily Copus
+
+## Dataset Card Contact
+ecopus@andrew.cmu.edu