| # 🌿 Plant Identification ViT (Fine-Tuned by Kelvin jackson (DRROBOT)) |
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| **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. |
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| 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 |
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| | 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 |
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| | Metric | Value | |
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| | **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 | |
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| 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 |
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| 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) |
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| Patch Size: 16x16 |
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| Embedding Dimension: 768 |
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| Heads: 12 |
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| Depth: 12 |
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| Fine-tuning Method: Full fine-tuning (not LoRA) |
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| ⚖️ License |
| This model is released under the Apache 2.0 License, allowing both commercial and research use with attribution. |
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| 💬 Citation |
| If you use this model, please cite: |
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| 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 |
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| Highly generalized on real-world photos |
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| Seamlessly integrates with multimodal LLMs |
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| Production-grade architecture suitable for cloud APIs |
