Update README.md

README.md

@@ -11,13 +11,13 @@ dataset_info:
           '1': trash
   splits:
   - name: original
-    num_bytes: 2095716.0
+    num_bytes: 2095716
     num_examples: 174
   - name: augmented
-    num_bytes: 32947958.0
+    num_bytes: 32947958
     num_examples: 522
   download_size: 35030975
-  dataset_size: 35043674.0
+  dataset_size: 35043674
 configs:
 - config_name: default
   data_files:
@@ -25,4 +25,86 @@ configs:
     path: data/original-*
   - split: augmented
     path: data/augmented-*
+license: mit
+task_categories:
+- image-classification
+language:
+- en
+pretty_name: 24-679 Image Dataset
+size_categories:
+- n<1K
 ---
+
+# Dataset Card for `ccm/2025-24679-image-dataset`
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset consists of images labeled as **recycling** (0) or **trash** (1). It was created as part of a classroom exercise in supervised learning and data augmentation, with the goal of giving students practice in building and evaluating image classification pipelines.  
+
+- **Curated by:** Fall 2025 24-679 course at Carnegie Mellon University  
+- **Shared by [optional]:** Christopher McComb  
+- **License:** MIT  
+- **Language(s):** N/A (image dataset)  
+
+## Uses
+
+### Direct Use
+
+- Training and evaluating image classification models (binary classification: recycling vs. trash).  
+- Experimenting with image preprocessing (resizing, normalization, augmentation).  
+- Teaching end-to-end ML workflows: data loading, training, validation, and evaluation.  
+
+### Out-of-Scope Use
+
+- Production deployment in real recycling or waste-sorting systems.  
+- Generalization to real-world trash/recycling classification without larger and more diverse datasets.  
+- Use in safety-critical or automated decision-making contexts.  
+
+## Dataset Structure
+
+The dataset includes two splits:  
+
+- **original**: 174 examples (collected by students).  
+- **augmented**: 522 examples (synthetically generated to balance and expand the dataset).  
+
+Each row includes:  
+
+- `image`: an image file (e.g., JPEG/PNG).  
+- `label`: integer class label (`0 = recycling`, `1 = trash`).  
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was curated to provide a simple, hands-on dataset for practicing image classification methods in an educational setting. Recycling/trash was chosen because it is easy to photograph and conceptually straightforward.  
+
+### Source Data
+
+#### Data Collection and Processing
+
+- Original images were collected on campus by students (e.g., photographs of bins, bottles, cans, paper, etc.).  
+- Labels were assigned manually during the collection process.  
+- Augmented data was generated with transformations such as rotations, flips, brightness/contrast changes, and cropping.  
+
+#### Who are the source data producers?
+
+- **Original data:** Students in the 24-679 course.  
+- **Augmented data:** Generated by course instructors and teaching assistants using standard augmentation tools.  
+
+## Bias, Risks, and Limitations
+
+- **Small sample size:** Only 174 original images.  
+- **Synthetic augmentation:** Does not capture real-world variation in lighting, backgrounds, or object diversity.  
+- **Domain bias:** Limited to CMU campus items, not representative of recycling/trash globally.  
+
+### Recommendations
+
+- Use primarily for teaching and demonstration purposes.  
+- Do not generalize beyond the dataset scope.  
+- Highlight dataset limitations during instruction to reinforce lessons about data quality and bias.  
+
+## Dataset Card Contact
+
+Christopher McComb (Carnegie Mellon University) — ccm@cmu.edu