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

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

📦 How to Use

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)

🧪 Supported Modes

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

📊 Performance Metrics

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

🧠 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

📖 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}}
}

📬 Contact & License

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

🤖 Hugging Face Integration

from transformers import AutoFeatureExtractor, TFModelForObjectDetection

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

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