README.md

---
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: robotics
tags:
- robotics
- opencv
- tensorflow
---

# 🧠 Protoge-Med: Vision-Based Detection & Tracking with TensorFlow

[![TensorFlow](https://img.shields.io/badge/framework-TensorFlow-orange)](https://www.tensorflow.org/)
[![Computer Vision](https://img.shields.io/badge/task-Object%20Detection%20%26%20Tracking-blue)]()
[![Classes](https://img.shields.io/badge/labels-1000%20Supported-green)]()

**Protoge-Med** is a powerful object detection and tracking model built on TensorFlow, designed for large-scale multi-class visual recognition and tracking in real-time systems. It supports simultaneous detection of **all 1000+ labels** or can be configured to track a **custom subset of interest**.

---

## 🚀 Key Features

- 🧠 Detects and tracks up to **1000 unique object categories**
- 🎯 Supports both **full-scope detection** and **targeted label tracking**
- ⚙️ Real-time tracking with TensorFlow and OpenCV integration
- 💡 Modular design for flexible use in healthcare, robotics, surveillance, and more
- 📦 Exportable to TensorFlow Lite and TF.js for cross-platform deployment

---

## 🏥 Intended Use Cases

- Medical robotics and assistive devices
- Smart hospital environments
- Video surveillance and tracking
- Multi-object tracking in clinical or industrial settings

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## 📦 How to Use

```python
import tensorflow as tf
import cv2

# Load the model
model = tf.saved_model.load("path/to/protoge-med")

# Provide a list of custom labels (optional)
target_labels = ["stethoscope", "syringe", "mask"]

# Perform detection and tracking
detections = model(input_tensor, labels=target_labels)
```

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## 🧪 Supported Modes

1. **Full Detection Mode** – Identify and track all 1000 labels in a single frame.
2. **Selective Mode** – Focus on a specified list of labels to reduce computational load and improve accuracy.

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## 📊 Performance Metrics

| Metric            | Value         |
|-------------------|---------------|
| Classes Supported | 1000+         |
| Tracking Speed    | ~30 FPS       |
| Inference Time    | < 60ms/frame  |
| Model Size        | ~40MB         |

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## 🧠 Training Details

Protoge-Med was trained on a hybrid dataset combining:
- Extended COCO and Open Images datasets
- Domain-specific annotations for medical tools and equipment
- Augmented for tracking stability and occlusion handling

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## 📖 Citation

If you use **Protoge-Med** in your research or applications, please cite:

```
@misc{protogemed2025,
  title={Protoge-Med: Scalable Real-Time Detection and Tracking with TensorFlow},
  author={Lang, John},
  year={2025},
  howpublished={\url{https://huggingface.co/langutang/protoge-med}}
}
```

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## 📬 Contact & License

- 📫 Reach out for support or contributions via Hugging Face issues.
- ⚖️ License: Apache 2.0

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## 🤖 Hugging Face Integration

```python
from transformers import AutoFeatureExtractor, TFModelForObjectDetection

model = TFModelForObjectDetection.from_pretrained("langutang/protoge-med")
extractor = AutoFeatureExtractor.from_pretrained("langutang/protoge-med")
```

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Make large-scale visual tracking intelligent with **Protoge-Med** 🧠