Duplicate from omasteam/waste-garbage-management-dataset

Co-authored-by: omasteam <omasteam@users.noreply.huggingface.co>

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README.md

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+---
+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)  
+
+
+---