| --- |
| language: en |
| tags: |
| - image-classification |
| - mnist |
| - emnist |
| - digit-recognition |
| - pytorch |
| - resnet |
| license: mit |
| datasets: |
| - mnist |
| - emnist |
| pipeline_tag: image-classification |
| --- |
| |
| # Handwritten Digit Classifier |
|
|
| A PyTorch image classification model that recognizes handwritten digits (0–9), built on a **pretrained ResNet-18** backbone (ImageNet weights) fine-tuned on a combined **MNIST + EMNIST** dataset with aggressive data augmentation. Achieves **99.46% accuracy** on the combined test set. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| | Property | Value | |
| |-----------------------|-------------------------------------------------| |
| | **Architecture** | ResNet-18 (pretrained on ImageNet) | |
| | **Framework** | PyTorch | |
| | **Task** | Image Classification (10 classes, digits 0–9) | |
| | **Input Size** | 32 × 32 (grayscale, converted to 3-channel) | |
| | **Output** | Softmax probabilities over digits 0–9 | |
| | **Test Accuracy** | **99.46%** | |
| | **Training Device** | CUDA (GPU) | |
| | **Epochs** | 7 | |
| | **Batch Size** | 256 | |
| | **Optimizer** | Adam (differential learning rates) | |
| | **Loss Function** | CrossEntropyLoss | |
| | **LR Scheduler** | StepLR (step=2, gamma=0.5) | |
|
|
| --- |
|
|
| ## Architecture |
|
|
| The model uses a **ResNet-18** backbone pretrained on ImageNet, with the default classification head replaced by a custom fully-connected head: |
|
|
| ``` |
| ResNet-18 Backbone (pretrained on ImageNet1K) |
| ↓ |
| Linear(512 → 128) |
| ↓ |
| ReLU() |
| ↓ |
| Dropout(0.3) |
| ↓ |
| Linear(128 → 10) |
| ↓ |
| Softmax (at inference) |
| ``` |
|
|
| **Differential learning rates** were used to preserve pretrained features while allowing the new head to learn faster: |
| - Pretrained backbone layers: `lr = 0.0001` |
| - New classification head (last 4 param groups): `lr = 0.001` |
|
|
| The dropout layer (p=0.3) reduces overfitting given the simplicity of digit images relative to the model's capacity. |
|
|
| --- |
|
|
| ## Dataset |
|
|
| The model was trained on a **combined MNIST + EMNIST (digits split)** dataset for greater diversity and robustness. |
|
|
| ### MNIST |
| | Property | Value | |
| |------------------|----------------------------| |
| | **Classes** | 10 (digits 0–9) | |
| | **Training set** | 60,000 grayscale images | |
| | **Test set** | 10,000 grayscale images | |
| | **Image size** | 28 × 28 pixels | |
| | **Source** | [yann.lecun.com/exdb/mnist](http://yann.lecun.com/exdb/mnist/) | |
|
|
| ### EMNIST (digits split) |
| | Property | Value | |
| |------------------|----------------------------| |
| | **Classes** | 10 (digits 0–9) | |
| | **Training set** | 240,000 grayscale images | |
| | **Test set** | 40,000 grayscale images | |
| | **Image size** | 28 × 28 pixels | |
| | **Source** | [NIST Special Database 19](https://www.nist.gov/itl/products-and-services/emnist-dataset) | |
|
|
| **Combined total:** 300,000 training images and 50,000 test images. |
|
|
| --- |
|
|
| ## Training |
|
|
| The model was trained for **7 epochs** on CUDA with a StepLR scheduler (halving LR every 2 epochs). Loss decreased consistently across all epochs. |
|
|
| | Epoch | Loss | |
| |-------|--------| |
| | 1 | 0.1732 | |
| | 2 | 0.0635 | |
| | 3 | 0.0446 | |
| | 4 | 0.0409 | |
| | 5 | 0.0340 | |
| | 6 | 0.0307 | |
| | 7 | 0.0279 | |
|
|
| **Final Test Accuracy: 99.46%** |
|
|
| --- |
|
|
| ## Data Augmentation |
|
|
| Aggressive augmentation was applied during training to improve generalization to real-world handwriting styles: |
|
|
| | Augmentation | Parameters | |
| |-------------------------|-----------------------------------------| |
| | Random Rotation | ±15° | |
| | Random Affine (translate)| ±15% horizontal and vertical | |
| | Random Affine (shear) | 10° | |
| | Random Perspective | distortion scale 0.3, p=0.3 | |
| | Color Jitter | brightness ±0.3, contrast ±0.3 | |
| | Normalization | mean (0.5, 0.5, 0.5), std (0.5, 0.5, 0.5) | |
|
|
| No augmentation was applied to the test set (only resize + normalize). |
|
|
| --- |
|
|
| ## Preprocessing |
|
|
| At inference, input images go through the following pipeline: |
|
|
| 1. Convert to **grayscale** |
| 2. **Invert** colors (white background → black background to match MNIST format) |
| 3. **Resize** to 32 × 32 |
| 4. Convert to **3-channel** (grayscale replicated across RGB channels for ResNet compatibility) |
| 5. **Normalize** with mean `(0.5, 0.5, 0.5)` and std `(0.5, 0.5, 0.5)` |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| import torch.nn as nn |
| from torchvision import transforms, models |
| from huggingface_hub import hf_hub_download |
| from PIL import Image |
| import numpy as np |
| |
| # Load model |
| model = models.resnet18(weights=None) |
| model.fc = nn.Sequential( |
| nn.Linear(512, 128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, 10) |
| ) |
| |
| weights_path = hf_hub_download( |
| repo_id="AdityaManojShinde/handwritten_digit_classifier", |
| filename="mnist_model.pth" |
| ) |
| model.load_state_dict(torch.load(weights_path, map_location="cpu")) |
| model.eval() |
| |
| # Preprocessing pipeline |
| transform = transforms.Compose([ |
| transforms.Grayscale(num_output_channels=3), |
| transforms.Resize((32, 32)), |
| transforms.ToTensor(), |
| transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), |
| ]) |
| |
| # Inference |
| image = Image.open("your_digit.png").convert("L") |
| img_array = 255 - np.array(image) # invert: white bg → black bg |
| image = Image.fromarray(img_array) |
| img_tensor = transform(image).unsqueeze(0) |
| |
| with torch.no_grad(): |
| output = model(img_tensor) |
| probs = torch.nn.functional.softmax(output, dim=1)[0] |
| predicted = probs.argmax().item() |
| |
| print(f"Predicted digit: {predicted} ({probs[predicted]*100:.1f}% confidence)") |
| ``` |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Works best with **centered, clearly written** single digits on a plain background. |
| - Not suitable for multi-digit recognition or digit detection in natural scenes. |
| - May struggle with highly stylized or non-standard digit handwriting not represented in MNIST/EMNIST. |
|
|
| --- |
|
|
| ## License |
|
|
| This model is released under the [MIT License](https://opensource.org/licenses/MIT). |
