jeromemassot/geosciences_images_classifier

Classify Geosciences Images

Geosciences images classifier

Classify Geosciences Images

Model Card for petroglyphs-nlp-consulting/GeoImages57kB7

This model classifies Geosciences images among 57 categories, related to Economic Geosciences, in particular for Oil and Gas applications.

Model Details

This model is based on EfficientNet(B0 and B7).

EfficientNet model trained on ImageNet-1k at resolution 600x600. It was introduced in the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le.

Model description

EfficientNet is a mobile-friendly pure convolutional model (ConvNet) that proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.

Model Description

• Developed by: Petroglyphs NLP Consulting
• Model type: EfficientNetB7
• Language(s): en
• License: gpl
• Finetuned from model: EfficientNetB0 / EfficientNetB7

Model Sources

• Repository: https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/
• Paper: https://arxiv.org/abs/1905.11946

Uses

Direct Use

This model can be used for classifying geosciences images into one of the 57 proposed categories.

Labels descrition = { 'ARE': 'Area Diagram', 'COR': 'Cores', 'CUT': 'Cuttings', 'DIS': 'Distributions as Bars', 'DST': 'DST Plot', 'DTB': 'Distribution as Tukey Boxes', 'FOS': 'Fossil Macroscopic', 'GE2': 'Geosciences 2D', 'GE3': 'Geosciences 3D', 'HBD': 'Horizontal Bar Diagram', 'HSD': 'Horizontal Bar Symmetrical', 'IN2': 'Installation Schema 2D', 'IN3': 'Installation Schema 3D', 'LCO': 'Logs Correlation', 'LEB': 'Colorbar Legend', 'LIM': 'Logs Imagery', 'LIN': 'Logs Interpreted', 'LOG': 'Logo', 'LSE': 'Logs Seismic', 'M2D': 'Model 2D', 'M3D': 'Model 3D', 'MAD': 'Map Administrative', 'MEQ': 'Equation', 'MGE': 'Map Geosciences', 'MIC': 'Optical Microscopy', 'MMO': 'Map Geomodel', 'MSE': 'Map Seismic', 'ORG': 'Organization', 'OUT': 'Outcrop', 'PAD': 'Production Area Diagram', 'PIE': 'Pie Chart', 'PLN': 'Project Diagram', 'SAT': 'Satellite Imagery', 'SCA': 'Scale Legend', 'SEM': 'Scanning Electronic Microscopy', 'SIA': 'Seismic with Attributes', 'SIG': 'Signature', 'SII': 'Seismic with Interpretations', 'SIR': 'Seismic Raw', 'STB': 'Stratigraphic Bar Chart', 'STL': 'Litho-Stratigraphic Diagram', 'STT': 'Stratigraphic Diagram', 'SUR': 'Equipment Surface', 'TAB': 'Table', 'TGN': 'Ternary Diagram', 'TSI': 'Thin-Section Microscopic', 'TSM': 'Thin-Section Macroscopic', 'TXT': 'Text Legend', 'VBD': 'Vertical Bar Diagram', 'VBU': 'Vertical Bar Uncertainty', 'VSD': 'Vertical Stacked Bar Diagram', 'WDS': 'Well Design', 'XPC': 'Geochemistry Plot', 'XPM': 'Cross-Plot Points & Curve', 'XPP': 'Cross-Plot Points', 'XPR': 'Polar Plot', 'XPV': 'Cross-Plot Curve' }

The model returns the 5 most probable categories with associated scores.

Downstream Use

Images embeddings could be used for other tasks such as automated labeling of additional images.

Environmental Impact

• Hardware Type: 2 x Titan RTX
• Hours used: 5
• Cloud Provider: Private Infrastructure
• Carbon Emitted: 0.6 kgCO2eq of which 0 percent were directly offset.

Citation

BibTeX:

@article{Tan2019EfficientNetRM,
  title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  author={Mingxing Tan and Quoc V. Le},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.11946}
}