---
license: mit
language:
- en
library_name: pytorch
pipeline_tag: image-classification
tags:
- dinov2
- vision-transformer
- vit
- image-classification
- garbage-classification
- waste-classification
- transfer-learning
- workshop
- pytorch

---
# DINOv2 Garbage Classification

A fine-tuned **DINOv2 ViT-L/14** model for **garbage image classification**.

This repository is prepared as a **workshop baseline model**, allowing participants to quickly start experimenting with transfer learning techniques without training a Vision Transformer from scratch.

The checkpoint can be used directly for inference or as an initialization for further fine-tuning on custom datasets.

---

# Overview

This project fine-tunes Meta AI's **DINOv2 ViT-L/14** backbone for a three-class garbage classification task.

Unlike conventional CNN-based approaches, DINOv2 learns powerful visual representations through self-supervised learning, making it an excellent backbone for downstream computer vision tasks with limited labeled data.

Participants are encouraged to extend this model with techniques such as:

- Hard Example Mining
- Active Learning
- Semi-supervised Learning
- Domain Adaptation
- Knowledge Distillation
- Custom Classification Heads
- Full Backbone Fine-tuning
- Test-Time Augmentation (TTA)

---

# Model Architecture

```
Input Image (518 × 518)
        │
        ▼
DINOv2 ViT-L/14 Backbone
        │
        ▼
1024-D Feature Vector
        │
        ▼
LayerNorm
        │
        ▼
Linear (1024 → 512)
        │
        ▼
GELU
        │
        ▼
Dropout (0.3)
        │
        ▼
Linear (512 → 3)
        │
        ▼
Prediction
```

## Backbone

- Base Model: `facebookresearch/dinov2`
- Variant: `dinov2_vitl14`
- Feature Dimension: **1024**
- Framework: **PyTorch**

Training strategy:

- Freeze entire backbone
- Unfreeze last **4 transformer blocks**
- Train custom classification head

---

# Dataset

The model classifies garbage into three categories.

| Label | Class |
|------:|-------|
| 0 | Recyclable |
| 1 | Electronic |
| 2 | Organic |

## Sample Images

### Recyclable

| | | | |
|:-:|:-:|:-:|:-:|
| ![](assets/0_Recyclable/1.jpg) | ![](assets/0_Recyclable/2.jpg) | ![](assets/0_Recyclable/3.jpg) | ![](assets/0_Recyclable/4.jpg) |

### Electronic

| | | | |
|:-:|:-:|:-:|:-:|
| ![](assets/1_Electronic/1.jpg) | ![](assets/1_Electronic/2.jpg) | ![](assets/1_Electronic/3.jpg) | ![](assets/1_Electronic/4.jpg) |

### Organic

| | | | |
|:-:|:-:|:-:|:-:|
| ![](assets/2_Organic/1.jpg) | ![](assets/2_Organic/2.jpg) | ![](assets/2_Organic/3.jpg) | ![](assets/2_Organic/4.jpg) |

---

# Dataset Statistics

| Split | Images |
|-------|-------:|
| Training | **23,873** |
| Validation | **2,653** |
| Test | **1,458** |

## Class Distribution

| Class | Images |
|-------|-------:|
| Recyclable | 12,567 |
| Electronic | 9,999 |
| Organic | 3,960 |

---

# Data Augmentation

Training images are augmented using:

- Random Resized Crop
- Horizontal Flip
- Color Jitter
- Gaussian Blur
- RandAugment
- ImageNet Normalization

Input resolution:

```
518 × 518
```

---

# Repository Structure

```
.
├── assets/
│   ├── 0_Recyclable/
│   ├── 1_Electronic/
│   └── 2_Organic/
│
├── best_dinov2.pth
└── README.md
```

---

# Load the Model

```python
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

pth_path = hf_hub_download(
    repo_id="galihkjaya/DINOv2-Garbage-Classification",
    filename="best_dinov2.pth"
)

class DINOv2Classifier(nn.Module):
    def __init__(self, num_classes=3, unfreeze_blocks=4):
        super().__init__()

        self.backbone = torch.hub.load(
            "facebookresearch/dinov2",
            "dinov2_vitl14"
        )

        for p in self.backbone.parameters():
            p.requires_grad = False

        for block in self.backbone.blocks[-unfreeze_blocks:]:
            for p in block.parameters():
                p.requires_grad = True

        self.head = nn.Sequential(
            nn.LayerNorm(1024),
            nn.Linear(1024, 512),
            nn.GELU(),
            nn.Dropout(0.3),
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        features = self.backbone(x)
        return self.head(features)

model = DINOv2Classifier()

model.load_state_dict(torch.load(pth_path, map_location="cpu"))
model.eval()
```

---

# Inference Example

```python
from PIL import Image
from torchvision import transforms

transform = transforms.Compose([
    transforms.Resize((518, 518)),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=(0.485,0.456,0.406),
        std=(0.229,0.224,0.225)
    )
])

labels = {
    0: "Recyclable",
    1: "Electronic",
    2: "Organic"
}

image = Image.open("sample.jpg").convert("RGB")
image = transform(image).unsqueeze(0)

with torch.no_grad():
    logits = model(image)
    pred = logits.argmax(1).item()

print(labels[pred])
```

---

# Future Improvement

This checkpoint serves as the baseline model. Suggested follow-up experiments include:

- Fine-tune all transformer blocks
- Hard Example Mining
- Replace the classification head
- Add new waste categories
- Train with Focal Loss
- Label Smoothing
- Test-Time Augmentation
- MixUp / CutMix
- Semi-supervised Learning
- Domain Adaptation
- Feature Extraction using DINOv2 embeddings

The objective is to demonstrate how a strong pretrained visual backbone can be adapted efficiently for downstream classification tasks.

---

# Limitations

- Only supports three waste categories.
- Performance depends on image quality and lighting conditions.
- Mixed-material waste may be difficult to classify.
- Intended for educational and research purposes.

---

# Acknowledgements

This project builds upon:

- **Meta AI** — DINOv2
- **PyTorch**
- **Hugging Face Hub**
- **Kaggle**

---

# Citation

```bibtex
@misc{galih2026,
  title={DINOv2 Garbage Classification},
  author={Galih Kusuma Wijaya},
  year={2026},
  publisher={Hugging Face},
  howpublished={https://huggingface.co/galihkjaya/DINOv2-Garbage-Classification}
}
```