Skip to main content

Minimal, well-documented CNN models for image classification. Great for beginners!

Project description

MiniCNN

MiniCNN is a minimal, well-documented Python package with beginner-friendly Convolutional Neural Network (CNN) models using PyTorch.
Great for learning, tutorials, and fast prototyping.

Installation

pip install MiniCNN

Usage

import torch

from MiniCNN import SimpleCNN, TinyCNN

Example: Create a SimpleCNN model for MNIST (10 classes)

model = SimpleCNN(num_classes=10)
print(model)

Random input (batch=1, channel=1, size=28x28)

x = torch.randn(1, 1, 28, 28)
y = model(x)
print(y.shape) # torch.Size()

Features

  • SimpleCNN: Standard small CNN, 2 conv layers + 2 FC layers.
  • TinyCNN: Extremely small model for quick tests.

Requirements

  • Python 3.8+
  • PyTorch >= 2.0.0

License

MIT License

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

minicnn-0.2.0.tar.gz (2.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

minicnn-0.2.0-py3-none-any.whl (2.7 kB view details)

Uploaded Python 3

File details

Details for the file minicnn-0.2.0.tar.gz.

File metadata

  • Download URL: minicnn-0.2.0.tar.gz
  • Upload date:
  • Size: 2.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for minicnn-0.2.0.tar.gz
Algorithm Hash digest
SHA256 97540eb4eea2689868f1f36405d8be5288d9a1c0ba2885987cb3b071ac3c6ec8
MD5 b8ded0cb3b43c1172dcf2ad2fd8acdc6
BLAKE2b-256 ac5a5e85e241e3df3d13fed0c27f34c47ce6926d3b6bad73234d3ee831423b88

See more details on using hashes here.

File details

Details for the file minicnn-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: minicnn-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 2.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for minicnn-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 b50c8d1a96c6f642201a7964e7e277b50de67b7fbfe7088811ec9c40678abb89
MD5 8fbc9e147252321e2289b1be0dccc874
BLAKE2b-256 97c95ead03fa2f9bc3fba7ced068f0dcadca6ab4486354c3040595e182fadb8f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page