TorchCNNBuilder

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Description in Russian is presented here.

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TorchCNNBuilder is an open-source framework for the automatic creation of CNN architectures. This framework should first of all help researchers in the applicability of CNN models for a huge range of tasks, taking over most of the writing of the architecture code. This framework is distributed under the 3-Clause BSD license. All the functionality is written only using pytorch (no third-party dependencies).

Installation

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The simplest way to install framework is using pip:

pip install torchcnnbuilder

Minimum technical requirements

The minimum system requirements for using the library are a Python interpreter version >3.9 and access to a computing system running Windows/Linux. The minimum hardware requirements include a processor (CPU) with 8 cores, 2GB of RAM, and 2GB of HDD storage, as well as a graphics processing unit (GPU) with 8 GB of VRAM, supporting CUDA version 7 or higher.

Additional packages for examples run

Please note that when running examples from the examples folder, additional libraries are used to visualize and generate the dataset:

pip install numpy
pip install pytorch_msssim
pip install matplotlib
pip install tqdm

They are not required for the library to work, so their installation is optional.

Usage

To initialize simple model with encoder-decoder architecture call ForecasterBase class:

from torchcnnbuilder.models import ForecasterBase

model = ForecasterBase(input_size=[H, W],
                       in_time_points=C_in,
                       out_time_points=C_out,
                       n_layers=5)

Where [H, W] - size of image in pixels, C_in - number of input channels, C_out - number of out_channels.

To operate separately with encoder and decoder parts they can be called from model:

encoder = model.encoder
decoder = model.decoder

For GPU mode (device="cuda"), only graphics cards supporting CUDA version >=7 can be used. Otherwise, only CPU mode is available (you must set device="cpu").

Examples

Extensive usage scenarios can be found in examples folder.

Components calls and usage in folder usage_examples.

Documentation

Check the documentation here.

Development

In order to check available local Makefile commands run in the project root:

make help

help: Show help for each of the Makefile recipes.
lint: Lint the project with flake8 lib.
doc: Build and run the doc locally.

Application Areas

TorchCNNBuilder enables CNN architectures for diverse real-world applications across multiple domains:

Environmental Monitoring

• Sea ice concentration forecasting
  Predict Arctic and Antarctic ice melt patterns to support climate research and maritime navigation safety using satellite imagery time series.

• Climate pattern recognition
  Analyze large-scale atmospheric data to identify emerging weather patterns, extreme event precursors, and long-term climate trends.

• Pollution level prediction
  Process multispectral sensor data to forecast air/water quality indices and identify pollution sources with spatial CNN architectures.

Remote Sensing

• Satellite image analysis
  Process high-resolution multispectral imagery for applications ranging from urban planning to precision agriculture using specialized encoder architectures.

• Land cover classification
  Automate large-scale terrain mapping with attention-based CNNs that handle spectral, spatial and temporal dimensions of data.

• Disaster monitoring
  Develop change detection systems that compare pre/post-event satellite imagery to assess flood, fire or earthquake damage in near-real-time.

Medical Imaging

• Automated diagnosis from X-ray/MRI scans
  Develop assistive diagnostic systems that can detect abnormalities in medical images with pixel-level precision while reducing radiologist workload.

• Tumor segmentation
  Create 3D convolutional networks for precise volumetric analysis of cancerous growths in CT/MRI scans.

• Medical time-series analysis
  Process sensor streams to predict patient deterioration through temporal features processing architectures.

Industrial Applications

• Predictive maintenance
  Monitor equipment vibration patterns and thermal signatures to forecast mechanical failures.

• Quality control in manufacturing
  Implement real-time visual inspection systems that detect defects in production lines.

Financial Forecasting

• Time-series prediction
  Build hybrid CNN-LSTM architectures that extract both spatial patterns from market heatmaps and temporal dependencies from price histories.

• Market trend analysis
  Process alternative data sources like satellite images of parking lots or social media sentiment through CNN architectures.

Sources

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• Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks
• Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble
• Post about framework development on habr.com - in russian

Contributing

• To join the project feel free to contact us;

• Issues and Pull Requests: submit bugs found or log feature requests.

Acknowledgement

The project is supported by FASIE - Foundation for Assistance to Small Innovative Enterprises.