Quickly Distinguish All Cores: A Multimodal Sequential Optical Feature Core Mineral Identification Model

Overview

GeoProtoNet is a deep learning architecture for multi-modal rock analysis, combining sequential image processing with geometric feature integration for comprehensive rock classification tasks.

Features

• Multi-Modal Integration: Processes XPL sequences and PPL images simultaneously
• Temporal Modeling: RWKV-based sequence processing for variable-length data
• Missing Modality Handling: Intelligent handling of missing data during inference
• Geometric Feature Integration: Combines visual and geometric data for enhanced classification
• Attention-Based Fusion: Cross-attention mechanism for effective modality fusion

Installation

# Clone the repository
git clone <repository-url>
cd Distinguish_all_cores

# Install dependencies
pip install torch torchvision
pip install -r requirements.txt

Quick Start

from model import GeoProtoNet

# Initialize model
model = GeoProtoNet(
    num_classes=10,
    geo_input_dim=4,
    feat_dim=512,
    pretrained=True,
    rwkv_layers=1
)

# Forward pass
logits = model(
    xpl_seq, xpl_masks,
    ppl_img, ppl_mask,
    geo_feat,
    valid_lens, has_ppl_mask
)

Model Architecture

Core Components

• RWKV Block: Temporal enhancement for sequential data
• Cross Attention Fusion: Multi-modal fusion with mask support
• GeoProtoNet: Main integration model with dual encoders

Data Format

Input Specifications

• xpl_seq: [B, T, 3, H, W] - XPL image sequences
• ppl_img: [B, 3, H, W] - PPL images
• geo_feat: [B, 4] - Geometric features
• masks: Binary masks for image regions

Output

• logits: [B, num_classes] - Classification scores

Configuration

# Model parameters
config = {
    'num_classes': 10,
    'geo_input_dim': 4,
    'feat_dim': 512,
    'pretrained': True,
    'rwkv_layers': 1,
    'num_heads': 8,
    'dropout': 0.1
}

Performance

• Accuracy: State-of-the-art results on rock classification benchmarks
• Efficiency: Linear complexity with respect to sequence length
• Robustness: Handles missing modalities gracefully

Requirements

• Python 3.8+
• PyTorch 1.9+
• torchvision
• numpy
• PIL

File Structure

Distinguish_all_cores/
├── model.py          # Model architecture
├── loss.py           # Prototypical loss function
├── train.py          # Training script
├── predict.py        # Inference script
├── dataloader.py     # Data loading utilities
└── README.md         # This file

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this code in your research, please cite:

@article{geoprotonet2024,
  title={Quickly Distinguish All Cores: A Multimodal Sequential Optical Feature Core Mineral Identification Model},
  author={Your Name},
  journal={Your Journal},
  year={2024}
}

Contact

For questions and support, please open an issue or contact [zkq0729@gmail.com].