| tags: | |
| - hojjatk/mnist-dataset | |
| - handwriting-recognition | |
| - classification | |
| - deep-learning | |
| metrics: | |
| accuracy: '0.96' | |
| precision: '0.96' | |
| recall: '0.96' | |
| dataset: | |
| name: hojjatk/mnist-dataset | |
| type: image | |
| license: mit | |
| downloads: | |
| count: 0 | |
| # Handwriting Recognition Model | |
| This is a trained model for handwriting recognition using **hojjatk/mnist-dataset** dataset. | |
| ## Usage | |
| ```python | |
| model = torch.load("mnsit_digit_nn") | |
| model.eval() | |
| ``` | |
| ## Training Param: | |
| epochs = 1 | |
| batch_size = 64 | |
| learning_rate = 0.001 | |
| ## Model Architectue: | |
| ['(conv1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))', '(conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))', '(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)', '(fc1): Linear(in_features=1568, out_features=128, bias=True)', '(fc2): Linear(in_features=128, out_features=10, bias=True)'] | |
| ## Evaluation Results | |
| - Accuracy: 0.96 | |
| - Precision: 0.96 | |
| - Recall: 0.96 | |