README.md

---
license: mit
datasets:
- TheDeepDas/Phytoplankton-Enumeration
language:
- en
base_model:
- microsoft/resnet-18
- google/mobilenet_v2_1.0_224
---

# 🌿 Phytoplankton Detection & Automation Pipeline

### 🧠 Repository Overview

This repository hosts a collection of **deep learning models** and **automation pipelines** designed for **phytoplankton detection and classification** from microscopic or satellite-based imagery.
The system is built to enable **end-to-end automation** — from raw image preprocessing to final ensemble-based predictions.

---

## 🧩 Model Collection

| Model                          | Type                | Description                                                                                | Use Case                                |
| :----------------------------- | :------------------ | :----------------------------------------------------------------------------------------- | :-------------------------------------- |
| **ResNet18**                   | 🧱 Base Model       | Standard CNN backbone trained for robust phytoplankton feature extraction.                 | Baseline accuracy and interpretability. |
| **MobileNetV2**                | ⚡ Lightweight Model | Efficient and fast model optimized for deployment on low-resource devices.                 | Real-time and edge applications.        |
| **Preprocessing Module**       | 🧼 Data Pipeline    | Handles denoising, resizing, augmentation, and normalization for optimal model input.      | Use before inference or training.       |
| **Full Pipeline (End-to-End)** | 🔄 Ensemble Model   | Combines preprocessing, model inference, and ensemble aggregation for automated detection. | Plug-and-play complete solution.        |

---

## ⚙️ Pipeline Architecture

```mermaid
graph TD
A[Raw Input Images] --> B[Preprocessing Module 🧼]
B --> C[ResNet18 🧱]
B --> D[MobileNetV2 ⚡]
C --> E[Feature Fusion & Ensemble 🔗]
D --> E
E --> F[Phytoplankton Class Prediction 🌿]
```

Each stage is modular, allowing researchers and developers to integrate or replace components as needed.

---

## 🧪 Training Details

* **Framework:** PyTorch
* **Optimizers:** Adam / SGD
* **Loss Function:** CrossEntropyLoss
* **Dataset:** Curated Phytoplankton Image Dataset (custom/preprocessed)
* **Input Size:** 224×224 RGB
* **Augmentations:** Random rotations, flips, and color normalization
* **Ensemble Strategy:** Weighted average of model logits

---

## 📊 Performance Summary

| Model         | R² Score | F1 Score | Notes                                                   |
| :------------ | :------- | :------- | :------------------------------------------------------ |
| ResNet18      | 0.897    | 0.93     | Strong baseline with balanced precision & recall.       |
| MobileNetV2   | 0.890    | 0.922    | Lighter and faster, ideal for deployment.               |
| Full Pipeline | **0.91** | **0.96** | Ensemble with preprocessing yields highest performance. |

---

## 💡 Key Features

* **🧼 Automated Preprocessing** — Handles normalization, resizing, and augmentation seamlessly.
* **🔗 Modular Ensemble** — Combine multiple architectures dynamically.
* **⚡ Lightweight & Deployable** — Optimized for both GPU and CPU inference.
* **🧬 Domain-Specific Optimization** — Tuned specifically for phytoplankton morphology and color features.

---

## 📘 Citation

If you use this work, please cite it as:

```
@misc{deepdas2025phytoplankton,
  author       = {Das, Deep},
  title        = {Phytoplankton Detection and Automation Pipeline},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/TheDeepDas/phytoplankton-Models}
}
```

---

## ❤️ Acknowledgements

Developed by **Deep Das**
🔗 [GitHub](https://github.com/THE-DEEPDAS) • [Hugging Face](https://huggingface.co/TheDeepDas) • [LinkedIn](https://www.linkedin.com/in/deep-das-4b5aa527b/)

---

> “Empowering ocean research through automation and intelligence.”