Create README.md

README.md

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+---
+license: mit
+---
+# Waste Identifer Classifcation Model 
+** By Amanda Sim 
+
+# Context 
+This classification model aims to identify items and categorize them based on how they should be disposed of. Using YOLOv11, this model fine-tunes previously trained datasets from Roboflow to fit new classes: recycle, trash, compost, and specialized disposal. This model is intented to be used to help people correctly dispose of their items and can be used for smart bins, which detected the item a person is holding and opens to the appropriate bin or for apps where the user can take a photo of the item and identify where it goes and how to dispose of it 
+
+# Training Data 
+- **Dataset 1**: Classifcation waste Computer Vision Model by GKHANG
+  -**Classes**: 10
+  -**Images**: 10,289
+  -**Link**: https://universe.roboflow.com/gkhang/classification-waste
+
+- **Dataset 2**: Trash Computer Vision Dataset by BAILE
+  -**Classes**: 48
+  -**Images**: 101
+  -**Link**: https://universe.roboflow.com/baile/trash-izcuy
+
+## Class Distribution 
+|Class                  | Count |
+|-----------------------|------:|
+|Recycle                | 1,607 |
+|Trash                  | 1,023 |
+|Compost                | 1,814 |
+|Specialized Disposal   | 1,026 |
+
+## Annotation Process 
+
+
+
+## Train/Validation/Test Split 
+- **Train**: 3,421 images (64%)
+- **Validation**: 1,145 images (21%)
+- **Test**: 791 images (15%)
+
+## Augmentations 
+- None
+
+# Training Procedure 
+- **Framework**: Ultralytics
+- **Hardware**: NVIDIA A100-SXM4-80GB
+- **Batch Size**: 64
+- **Epochs**: 50
+- **Image Size**: 640
+- **Patience**: 10
+
+- _Early Stopping_: 38 epochs
+
+# Evaluation Results 
+
+
+# Limitations and Biases