# 🌿 Plant Identification ViT (Fine-Tuned by Kelvin jackson (DRROBOT))

**Base Model:** [`marwaALzaabi/plant-identification-vit`](https://huggingface.co/marwaALzaabi/plant-identification-vit)  
**Fine-Tuned On:** [Kaggle – House Plant Species Dataset](https://www.kaggle.com/datasets/jonasnevers/house-plant-species)  
**Developed By:** [Kelvin Nnadi](https://huggingface.co/your-username)  
**Objective:** To build a high-accuracy computer vision model that can identify and describe a wide range of houseplants, forming the perception layer of a larger AI botanist system.

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## 🧠 Model Summary
This model is a **fine-tuned Vision Transformer (ViT)** specialized for **plant species recognition**.  
It was trained on **14,790 high-quality images** covering **47 distinct houseplant species**, improving the model’s ability to handle real-world lighting, angles, and background variation.

The model forms the **visual foundation** of an intelligent AI system that integrates with **Qwen Instruct** for reasoning, allowing users to snap or upload plant photos and receive detailed botanical explanations.

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## ⚙️ Training Details

| Parameter | Value |
|------------|--------|
| **Base Model** | `marwaALzaabi/plant-identification-vit` |
| **Dataset** | Kaggle House Plant Species (~14.8k images, 47 classes) |
| **Epochs** | 5 |
| **Batch Size** | 16 |
| **Optimizer** | AdamW |
| **Learning Rate** | 5e-5 |
| **Scheduler** | Cosine Annealing |
| **Hardware** | NVIDIA T4 GPU (Colab Pro+) |
| **Mixed Precision** | FP16 enabled |
| **Framework** | Hugging Face Transformers + PyTorch |

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## 📈 Performance Metrics

| Metric | Value |
|---------|-------|
| **Training Loss (Final)** | 0.0010 |
| **Validation Loss (Final)** | 0.2161 |
| **Best Validation Epoch** | 5 |
| **Global Training Loss** | 0.1849 |
| **Steps** | 8,320 |
| **Samples/Sec** | 7.75 |
| **Steps/Sec** | 0.969 |

The model achieved **remarkably low loss** and stable convergence, indicating excellent generalization to unseen plant images.

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## 🌱 Intended Use
This model can be used for:
- 📸 **Real-time plant species recognition** from photos  
- 🌿 **Agricultural or botanical assistant systems** (e.g., Farmlingua or AI Botanist)  
- 🧠 **Educational tools** for plant taxonomy learning  
- 🪴 **Smart garden applications** with vision intelligence  

It can also be **paired with a text-based reasoning model** like  to provide rich, natural language explanations about plant care, origin, and characteristics.

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🧩 Model Architecture
Type: Vision Transformer (ViT)

Patch Size: 16x16

Embedding Dimension: 768

Heads: 12

Depth: 12

Fine-tuning Method: Full fine-tuning (not LoRA)

⚖️ License
This model is released under the Apache 2.0 License, allowing both commercial and research use with attribution.

💬 Citation
If you use this model, please cite:

java
Copy code
@model{kelvinnnadi_plant_vit_2025,
  title={Plant Identification ViT (Fine-Tuned)},
  author={Kelvin Nnadi},
  year={2025},
  howpublished={Hugging Face},
  url={https://huggingface.co/your-username/plant-identification-vit-finetuned}
}
🏆 Highlights
Fine-tuned with 47 classes of houseplants

Highly generalized on real-world photos

Seamlessly integrates with multimodal LLMs

Production-grade architecture suitable for cloud APIs