Update README.md

README.md

@@ -15,4 +15,139 @@ tags:
 - 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.
+