Train YOLOv11 Model + Web Dashboard Creation

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## **Project: YOLOv11 Model Training + Web Dashboard With Image Metadata & GPS Mapping**

### **Overview**
I am looking for an experienced AI/ML developer who can train a **YOLOv11 object detection model** using a **predefined image dataset** (which I will provide). The project also requires building a **simple single‑page web interface** that displays image statistics, metadata, and GPS coordinates extracted from drone‑captured images. These coordinates must be plotted on **Google Maps**, showing markers that correspond to each image.

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## **Project Requirements**

### **1. YOLOv11 Model Development**
- Train a **YOLOv11** model using the provided dataset.
- Perform data preprocessing, augmentation, and annotation validation if needed.
- Provide:
- Trained model weights
- Inference script
- Documentation on how to run the model

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### **2. Image Metadata Extraction**
Each image contains metadata including:
- GPS coordinates (latitude & longitude)
- Timestamp
- Camera/drone information (if available)

The system should:
- Automatically extract metadata from each image
- Display metadata in a clean, readable format on the web page

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### **3. Web Page Requirements**
A simple, clean **single‑page web application** that includes:

#### **Image Stats Section**
- Total number of images
- Detected objects summary (from YOLOv11 inference)
- Confidence scores
- Any additional useful statistics

#### **Image Metadata Viewer**
- Display metadata for each image
- Show extracted GPS coordinates

#### **Google Maps Integration**
- Plot each image’s GPS location on Google Maps
- Each marker should:
- Represent an image capture point
- Show a small popup with the image thumbnail + metadata when clicked

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### **4. Deliverables**
- Fully trained YOLOv11 model + weights
- Source code for:
- Model training
- Inference pipeline
- Metadata extraction
- Web page (HTML/CSS/JS or a simple framework like Flask, Django, Node.js, etc.)
- Google Maps integration with API key placeholder
- Documentation for setup and usage

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### **5. Skills Required**
- Python (PyTorch, Ultralytics YOLO)
- Machine Learning / Computer Vision
- Web development (basic front‑end + simple backend)
- Experience with Google Maps API
- Metadata extraction (EXIF, GPS tags)

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### **6. Additional Notes**
- Dataset will be provided after project award.
- Clean, well‑commented code is required.
- Preference for someone who has worked with YOLO models before.

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