| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| - Metal |
| - Glass |
| - Biological |
| - Paper |
| - Battery |
| - Trash |
| - Cardboard |
| - Shoes |
| - Clothes |
| - Plastic |
| splits: |
| - name: train |
| num_examples: 19762 |
| total_num_examples: 19762 |
| task_templates: |
| - task: image-classification |
| input_schema: image |
| label_schema: class_label |
| license: mit |
| tags: |
| - waste |
| - garbage |
| - waste-management |
| - cnn |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Garbage Classification Dataset |
|
|
| ## Dataset Summary |
| This dataset contains images of garbage items categorized into **10 classes**, designed for machine learning and computer vision projects focusing on **recycling and waste management**. |
|
|
| It is ideal for building classification or object detection models, or developing **AI-powered solutions for sustainable waste disposal**. |
|
|
| - **Total Images:** 19,762 |
| - **Number of Classes:** 10 |
|
|
| ### Class Distribution |
| - **Metal:** 1020 |
| - **Glass:** 3061 |
| - **Biological:** 997 |
| - **Paper:** 1680 |
| - **Battery:** 944 |
| - **Trash:** 947 |
| - **Cardboard:** 1825 |
| - **Shoes:** 1977 |
| - **Clothes:** 5327 |
| - **Plastic:** 1984 |
|
|
| --- |
|
|
| ## Key Features |
| - **Diverse Categories:** Covers common household waste items for a wide range of applications. |
| - **Balanced Distribution:** Each class is sufficiently populated, ensuring robust model training. |
| - **High-Quality Images:** Clear and well-annotated images for better performance in computer vision tasks. |
| - **Real-World Applications:** Ideal for recycling solutions, waste segregation apps, and educational tools. |
|
|
| --- |
|
|
| ## Academic Reference |
| This dataset was featured in the research paper: |
| **_"Managing Household Waste Through Transfer Learning"_** |
| It demonstrates the dataset’s utility in **real-world waste management applications**. Researchers and developers can replicate or extend the experiments for further studies. |
|
|
| --- |
|
|
| ## 🔗 Parquet version |
|
|
| This dataset is automatically converted to [Apache Parquet](https://parquet.apache.org/) by the Hugging Face parquet-converter bot. |
| You can find the Parquet files here: |
|
|
| 👉 [View Parquet files](https://huggingface.co/datasets/omasteam/waste-garbage-management-dataset/tree/refs%2Fconvert%2Fparquet) |
|
|
| Using the Parquet version is often faster for loading and querying metadata. |
|
|
| ### Example usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load metadata from the parquet-converted branch |
| dataset = load_dataset("omasteam/waste-garbage-management-dataset", split="train") |
| |
| print(dataset[0]) |
| ``` |
|
|
| --- |
|
|
|
|
| ## Applications |
| - **AI for Sustainability:** Train AI models to classify garbage and promote automated waste management. |
| - **Recycling Programs:** Build systems to sort garbage into recyclable and non-recyclable materials. |
| - **Environmental Education:** Develop tools to teach kids and adults about proper waste disposal. |
|
|
| --- |
|
|
| ## Feedback |
| Thank you for your interest in our waste dataset! |
| Whether you have used the dataset or are considering its use, your feedback is crucial. Please share your thoughts and experiences to help us improve. |
|
|
| --- |
|
|
| ## Citation |
| If you use this dataset, please cite the following: |
|
|
| **Author:** *Suman Kunwar* |
| **Company:** *D.Waste.app* |
| **App Link:** [Deep Waste - Play Store](https://play.google.com/store/apps/details?id=com.hai.deep_waste&hl=en) |
|
|
|
|
| --- |
