protege-med

🧠 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.

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🚀 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

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🏥 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

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

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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

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 🧠