README.md

---
license: mit
datasets:
- Sisigoks/Planter_GARDEN_EDITION
language:
- en
metrics:
- accuracy
base_model:
- google/vit-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- biology
- plants
- flora
- 10K
---

# 🌿 Sisigoks/FloraSense

**FloraSense** is a fine-tuned Vision Transformer (ViT) model designed for accurate classification of plant species and flora-related imagery. It builds on top of the powerful `google/vit-base-patch16-224` base model and is fine-tuned on the **Planter_GARDEN_EDITION** dataset curated by [Sisigoks](https://huggingface.co/Sisigoks), which includes over 10,000 diverse plant images.

---

## 🧠 Model Description

- **Architecture**: Vision Transformer (ViT)
- **Base Model**: [`google/vit-base-patch16-224`](https://huggingface.co/google/vit-base-patch16-224)
- **Task**: Image Classification
- **Use Case**: Automated plant and flora species recognition in digital botany, garden classification systems, plant care apps, biodiversity projects, and educational tools.

---

## 📊 Model Performance

- **Evaluation Accuracy**: **35.46%**
- **Evaluation Loss**: 4.2894
- **Epochs Trained**: 10
- **Evaluation Speed**: 
  - 33.9 samples/sec
  - 2.12 steps/sec

> ⚠️ While the accuracy may appear moderate, the model is handling over **10,000** highly similar plant species, making this a non-trivial challenge in fine-grained classification.

---

## 🧪 Training Procedure

| Hyperparameter        | Value                      |
|-----------------------|----------------------------|
| Learning Rate         | 5e-5                       |
| Train Batch Size      | 16                         |
| Eval Batch Size       | 16                         |
| Gradient Accumulation | 4                          |
| Total Effective Batch | 64                         |
| Optimizer             | Adam (β1=0.9, β2=0.999)    |
| Scheduler             | Linear w/ warmup (10%)     |
| Epochs                | 15                         |
| Seed                  | 42                         |

- **Framework**: PyTorch
- **Libraries**: Transformers 4.45.1, Datasets 3.0.1, Tokenizers 0.20.0

---

## 📚 Dataset

- **Name**: [`Sisigoks/Planter_GARDEN_EDITION`](https://huggingface.co/datasets/Sisigoks/Planter_GARDEN_EDITION)
- **Type**: Image Classification
- **Language**: English
- **Scope**: Over 10,000 unique plant and floral species
- **Format**: Real-world garden and nature photography
- **Use Case**: Realistic and diverse training scenarios for classification models

---

## ✅ Intended Use

### Use Cases

- Botanical image recognition apps
- Educational tools for students and researchers
- Smart gardening & plant care solutions
- Field-use flora identification via AR and mobile apps

### Target Users

- Botanists
- AI and ML researchers
- Gardeners and farmers
- Biology educators and students

---

## ⚠️ Limitations

- May confuse visually similar species due to fine-grained class diversity.
- Performance could degrade in poor lighting or occlusion-heavy environments.
- Biases may exist based on the geographic scope of the dataset (e.g., underrepresentation of tropical or rare plants).

---

## 🔐 Ethical Considerations

- **Accuracy**: Misclassification of medicinal/toxic plants can have real-world safety implications.
- **Bias**: Regional, lighting, or season-specific training data may skew predictions in certain environments.
- **Usage**: This is a research-grade model and should not be relied on for critical decisions without expert validation.

---

## 🚀 How to Use

``` python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

# Load model and processor
processor = AutoImageProcessor.from_pretrained("Sisigoks/FloraSense")
model = AutoModelForImageClassification.from_pretrained("Sisigoks/FloraSense")

# Load and preprocess image
image = Image.open("your_image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_label = logits.argmax(-1).item()

print(f"Predicted class ID: {predicted_label}")
```

## 📄 Citation
If you use this model or dataset in your work, please cite:

```
  @misc{sisigoks_florasense_2025,
    author = {Sisigoks},
    title = {FloraSense: ViT-based Fine-Grained Plant Classifier},
    year = {2025},
    publisher = {Hugging Face},
    howpublished = {\url{https://huggingface.co/Sisigoks/FloraSense}}
  }
```

## 🙌 Acknowledgements

- Hugging Face 🤗 – for providing the model and dataset hosting infrastructure.
- Google Research – for the original ViT architecture that enabled scalable vision transformers.