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BIONEXUS_PLANT_CLASSIFICATION

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+# 🌿 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.
+---
+## 🧠 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.
+---
+## ⚙️ 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 |
+---
+## 📈 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.
+---
+## 🌱 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.
+---
+🧩 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