---
license: agpl-3.0
library_name: ultralytics
pipeline_tag: image-classification
datasets:
  - Pramudit/Waste_SIP_Dataset
metrics:
  - accuracy
base_model: Ultralytics/YOLOv8
tags:
  - ultralytics
  - yolov8
  - yolov8-cls
  - image-classification
  - waste-classification
  - waste-sorting
  - recycling
---

# Waste Classifier (Bio vs Non-Bio) — YOLOv8s-cls

A lightweight image classifier that sorts waste photos into **biodegradable (`Bio`)** and **non-biodegradable (`Non_Bio`)**. It is [YOLOv8s-cls](https://docs.ultralytics.com/tasks/classify/) fine-tuned from the COCO/ImageNet-pretrained checkpoint on the [Waste_SIP_Dataset](https://huggingface.co/datasets/Pramudit/Waste_SIP_Dataset).

- **Task:** binary image classification
- **Classes:** `Bio`, `Non_Bio`
- **Architecture:** YOLOv8s-cls (~10 MB checkpoint)
- **Input size:** 224 × 224
- **Best validation top-1 accuracy:** 0.96 (epoch 19 of 20)

## Quick start

```bash
pip install ultralytics huggingface_hub
```

```python
from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(repo_id="W4ashabii/waste_classifier", filename="best.pt")
model = YOLO(weights)

results = model.predict("your_image.jpg", imgsz=224)
r = results[0]
print(r.names[r.probs.top1], float(r.probs.top1conf))
```

Batch prediction on a folder:

```python
results = model.predict("path/to/images/", imgsz=224)
for r in results:
    print(r.path, r.names[r.probs.top1], round(float(r.probs.top1conf), 3))
```

## Results

Metrics from the validation split, using the best checkpoint (`best.pt`, epoch 19):

| Metric | Value |
|---|---|
| Top-1 accuracy | **0.960** |
| Final-epoch (20) top-1 accuracy | 0.955 |
| Final training loss | 0.038 |

Per-class recall (from the normalized confusion matrix):

| Class | Recall |
|---|---|
| Bio | 0.97 |
| Non_Bio | 0.95 |

Top-5 accuracy is always 1.0 because there are only two classes, so it is not informative.

Training lasted about 2.3 minutes (140 s for 20 epochs).

![Training curves](results.png)
![Normalized confusion matrix](confusion_matrix_normalized.png)

> Note: `best.pt` was chosen by validation accuracy, so the validation numbers above are slightly optimistic. For an unbiased estimate, evaluate on the dataset's held-out `test` split.

## Training details

| Setting | Value |
|---|---|
| Base model | `yolov8s-cls.pt` (pretrained) |
| Framework | Ultralytics YOLOv8 |
| Epochs | 20 (early-stopping patience 5) |
| Image size | 224 |
| Batch size | 64 |
| Optimizer | AdamW |
| Initial learning rate | 1e-3, cosine schedule |
| Precision | FP16 mixed precision (AMP) |
| Hardware | NVIDIA RTX 4070 (8 GB) |

## Dataset

Trained on [Pramudit/Waste_SIP_Dataset](https://huggingface.co/datasets/Pramudit/Waste_SIP_Dataset), an image-folder dataset with `train`, `validation` and `test` splits and two classes (`Bio`, `Non_Bio`). Please refer to the dataset page for its license, collection method and class distribution.

## Intended use

- Demos, teaching and prototyping of automated waste sorting
- A baseline for waste-classification research
- Starting point for further fine-tuning on your own waste images

## Limitations

- **Binary only.** The model tells biodegradable from non-biodegradable. It does not identify material types (plastic, glass, metal, paper) and does not detect or locate objects in an image.
- **Single-object assumption.** It classifies the whole image, so photos with mixed waste or cluttered scenes may give unreliable results.
- **Domain shift.** Accuracy was measured on images from one dataset. Performance can drop with different cameras, lighting, backgrounds, or waste types that are rare in the training data.
- **Not safety-critical.** Do not rely on it as the sole decision-maker for real waste handling, hygiene or compliance.

## License

The weights are released under **AGPL-3.0**, in line with the Ultralytics YOLOv8 license. The training data has its own license; check the dataset page before commercial use.

## Citation

```bibtex
@software{jocher2023ultralytics,
  author  = {Jocher, Glenn and Chaurasia, Ayush and Qiu, Jing},
  title   = {Ultralytics YOLOv8},
  version = {8.0.0},
  year    = {2023},
  url     = {https://github.com/ultralytics/ultralytics},
  license = {AGPL-3.0}
}
```