🌿 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, which includes over 10,000 diverse plant images.
🧠 Model Description
- Architecture: Vision Transformer (ViT)
- Base Model:
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 - 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
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.