https://github.com/bin-detective/bindetective-ml
This repository provides the machine learning models for the Bin Detective app, designed to classify waste into 10 categories. It includes model details, training documentation, and dataset information to support waste classification tasks.
https://github.com/bin-detective/bindetective-ml
cnn efficientnet keras resnet tensorflow
Last synced: 3 months ago
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This repository provides the machine learning models for the Bin Detective app, designed to classify waste into 10 categories. It includes model details, training documentation, and dataset information to support waste classification tasks.
- Host: GitHub
- URL: https://github.com/bin-detective/bindetective-ml
- Owner: Bin-Detective
- Created: 2024-11-21T16:01:48.000Z (over 1 year ago)
- Default Branch: main-efficientnetv2s
- Last Pushed: 2024-12-12T16:31:40.000Z (over 1 year ago)
- Last Synced: 2025-06-05T07:36:39.786Z (about 1 year ago)
- Topics: cnn, efficientnet, keras, resnet, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 16.4 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# `Robin` BinDetective Waste Classification Model
Robin is a model developed for waste classification, categorizing items into 10 distinct classes. The model has been fine-tuned using different architectures to find the best and optimal performance. Below, you’ll find links to specific branches for each model variant.
---
## **Available Models**
Each model branch contains `model information`, `documentation`, `training and validation details`.
| **Variant** | **Branch** | **Model** |
|-------------------|-------------------------------------------------------|--------------------------------------------------------------------------------------------------|
| **EfficientNetV2S**| [main-efficientnetv2s](https://github.com/Bin-Detective/bindetective-ml/tree/main-efficientnetv2s) | [Kaggle](https://www.kaggle.com/models/bahiskaraananda/robin-efficientnetv2s) |
| **EfficientNetB0** | [efficientnetb0](https://github.com/Bin-Detective/bindetective-ml/tree/efficientnetb0) | |
| **ResNet50** | [mango-resnet50](https://github.com/Bin-Detective/bindetective-ml/tree/mango-resnet50) | [Kaggle](https://www.kaggle.com/models/bahiskaraananda/robin-resnet50) |
## Classes
1. buah_sayuran (Fruits/Vegetables): `Fruit/vegetable waste, peels, seeds`
2. daun (Leaves): `Leaves, flowers, grass, garden waste`
3. elektronik (Electronics): `Phones, pcb, cables, batteries, etc`
4. kaca (Glass): `Glass bottles, broken glass`
5. kertas (Paper): `Newspapers, cardboard, paper waste`
6. logam (Metal): `Cans, forks, spoons`
7. makanan (Food): `Food remnants, expired foods`
8. medis (Medical): `Medical waste, masks, gloves, etc`
9. plastik (Plastic): `Bottles, straw, plastic bags, etc`
10. tekstil (Textile): `Clothes, fabric`
## Datasets
```bash
robin-base/
├── train/
│ ├── buah_sayuran/
│ ├── daun/
│ ├── elektronik/
│ └── ...
├── val/
│ ├── buah_sayuran/
│ ├── daun/
│ ├── elektronik/
│ └── ...
└── test/
├── buah_sayuran/
├── daun/
├── elektronik/
└── ...
```
The dataset is balanced with a 1:1 ratio for each class and split into `70/20/10` for training, validation, and testing, consisting of 30,000 images in total.
- Training: `20990 images`
- Validation: `6000 images`
- Test: `3010 images`
- robin-datasets
- preprocessing-documentation
## Model Details
- Base model: `EfficientNetV2S (pretrained on ImageNet)`
- Frozen Layers: `Initial layers up to block 143 (block4f_bn)`
- Custom layers: `Global Average Pooling, Dropout (0.5), Dense (10 classes, softmax)`
- Input size: `224,224,3`
- Optimizer: `Adam`
- Loss: `Categorical Crossentropy`
```bash
Total params: 20344170 (77.61 MB)
Trainable params: 18468850 (70.45 MB)
Non-trainable params: 1875320 (7.15 MB)
```
- robin-efficientnetv2s-model
- robin-efficientnetv2s-training-documentation
## Performance
The current `robin_efficientnetv2s` test accuracy and performance metrics for each class are as follows:
```bash
precision recall f1-score support
buah_sayuran 0.95 0.91 0.93 301
daun 0.98 0.98 0.98 301
elektronik 0.99 0.98 0.99 301
kaca 0.94 0.97 0.95 301
kertas 0.96 0.96 0.96 301
logam 0.94 0.97 0.95 301
makanan 0.91 0.95 0.93 301
medis 0.97 0.97 0.97 301
plastik 0.95 0.89 0.92 301
tekstil 0.99 0.99 0.99 301
accuracy 0.96 3010
macro avg 0.96 0.96 0.96 3010
weighted avg 0.96 0.96 0.96 3010
```
Confusion matrix:
## Requirements
- TensorFlow `2.15`
- Python `3.x`
- Keras
- NumPy
## Workflow
Here’s a simple explanation of the workflow for the Robin model:
```plaintext
+-------------------+ +-----------------------------+ +-----------------------------+
| Collect Dataset | -----> | Preprocess Dataset | -----> | Model Development |
| (30000+ images) | | - Resize images to 224x224 | | - Select base architecture |
+-------------------+ | - Normalize pixel values | | - Load pretrained weights |
| - Balance each dataset | | - Freeze initial layers |
| class (1:1) | | - Add custom layers |
| - Split into Train/Val/Test | | - Compile with optimizer |
| (70/20/10 ratio) | | and loss function |
+-----------------------------+ +-----------------------------+
|
v
+-----------------------------+ +------------------------------+
| Model Evaluation | <----- | Model Training |
| - Test on 10% dataset | | - Train on 70% dataset |
| - Generate performance | | - Validate on 20% dataset |
| metrics | | - Fine-tune |
+-----------------------------+ | - Save best-performing model |
| +------------------------------+
v
+--------------------------------+
| Troubleshoot |
| - Adjust hyperparameters |
| - Fine-tune and analyze |
| performance for improvements |
+--------------------------------+
|
v
+----------------------------------+
| Deploy the Model |
| - Save the model in TensorFlow |
| SavedModel format |
| - Deploy the model using FastAPI |
| for waste classification |
+----------------------------------+
```