---
tags:
- clip
- waste-classification
- image-classification
- pytorch
- finetuned
license: mit
language:
- en
base_model: openai/clip-vit-base-patch16
datasets:
- recyclable-and-household-waste-classification
metrics:
- accuracy
model-index:
- name: openclip-finetune-waste
  results:
  - task:
      type: image-classification
      name: Waste Classification
    dataset:
      type: recyclable-and-household-waste-classification
      name: Recyclable and Household Waste Classification
    metrics:
    - type: accuracy
      value: 0.9133
      name: Validation Accuracy
---

# Finetuned CLIP for Waste Classification

This model is a finetuned version of OpenAI's CLIP ViT-B/16 for waste classification.

## Model Details

- **Model Name**: ViT-B-16
- **Pretrained**: laion2b_s34b_b88k
- **Classes**: 30 waste categories
- **Validation Accuracy**: 0.9133

## Classes

The model can classify the following waste items:
aerosol_cans, aluminum_food_cans, aluminum_soda_cans, cardboard_boxes, cardboard_packaging, clothing, coffee_grounds, disposable_plastic_cutlery, eggshells, food_waste, glass_beverage_bottles, glass_cosmetic_containers, glass_food_jars, magazines, newspaper, office_paper, paper_cups, plastic_cup_lids, plastic_detergent_bottles, plastic_food_containers, plastic_shopping_bags, plastic_soda_bottles, plastic_straws, plastic_trash_bags, plastic_water_bottles, shoes, steel_food_cans, styrofoam_cups, styrofoam_food_containers, tea_bags

## Usage

```python
from clip_waste_classifier.finetuned_classifier import FinetunedCLIPWasteClassifier

# Load model from Hugging Face Hub
classifier = FinetunedCLIPWasteClassifier(hf_model_id="ysfad/openclip-finetune-waste")

# Classify image
result = classifier.classify_image("path/to/image.jpg")
print(f"Predicted: {result['predicted_item']} ({result['best_confidence']:.3f})")
```

## Training

This model was finetuned on the [Recyclable and Household Waste Classification](https://www.kaggle.com/datasets/alistairking/recyclable-and-household-waste-classification) dataset with:

- 15,000 images across 30 waste categories
- 15 epochs of training
- Batch size: 16
- Learning rate: 5e-6
- Train/Val/Test split: 70%/10%/20%

## License

This model is released under the MIT License.