Handwritten Digit Recognition

Dataset Overview

• Name: HwD‑1000 dataset
• Link: https://github.com/niklashenning/hwd-1000-dataset
• Description: Contains 1,000 images of handwritten digits (0‑9). Images are 28 × 28 pixels, white background, black pen strokes, with varying styles and thicknesses.

Usage

• Training Models: Train neural networks using PyTorch and the Tensor library.
• Data Processing: Load and process the dataset with pandas, load and transform images with Pillow, visualize training results with Matplotlib.

Training Results

• Training Settings:
  • Epochs: 50
  • Learning Rate: 0.001
  • Optimizer: AdamW
  • Loss Function: CrossEntropyLoss
  • Split: 80 % train, 20 % validation
• Validation Accuracy: 99.50 %
• Test Accuracy: 99.50 % (199/200)

Training Loss

License

• License Type: MIT license