---
license: apache-2.0
library_name: pytorch
pipeline_tag: image-classification
tags:
  - executorch
  - zephyr
  - tinyml
  - mnist
  - cortex-m
  - esp32
  - int8
datasets:
  - ylecun/mnist
metrics:
  - accuracy
model-index:
  - name: TinyDigitNet
    results:
      - task:
          type: image-classification
          name: Handwritten digit recognition
        dataset:
          type: ylecun/mnist
          name: MNIST test set
        metrics:
          - type: accuracy
            value: 0.9746
            name: Top-1 accuracy (fp32, 10,000 test images)
---

# TinyDigitNet

A 14,410-parameter CNN for 28x28 grayscale digits, sized to run under
[ExecuTorch](https://github.com/pytorch/executorch) on Zephyr RTOS boards with no
delegate: it has been run on an RP2040 (Cortex-M0+, Arducam Pico4ML-BLE, reading digits
from the on-board camera), an ESP32-S3 (Seeed XIAO ESP32S3 Sense), an ESP32 (M5Stack ATOM)
and Cortex-M33 parts (Arduino UNO Q, nRF5340, MCX N947).

```
conv1 1->8 3x3, ReLU, maxpool 2      28x28 -> 13x13
conv2 8->16 3x3, ReLU, maxpool 2     13x13 -> 5x5
fc1 400->32, ReLU
fc2 32->10
```

Input: `(1, 1, 28, 28)` float, pixel/255 normalised with mean 0.1307 and std 0.3081
(the usual MNIST constants; the firmware applies the same transform to camera crops).
Output: 10 logits.

## Files

| File | What |
|---|---|
| `tiny_digit_net.py` | model definition (PyTorch) |
| `tiny_digit_net.pt` | trained state dict, fp32 |
| `executorch/fp32.pte` | ExecuTorch program, portable fp32 kernels (62.8 KB) |
| `executorch/int8-cmsis-m0plus.pte`, `-m4.pte`, `-m33.pte`, `-m7.pte` | int8 via the ExecuTorch Cortex-M quantizer and `CortexMPassManager`, one per core (25.4 KB) |
| `tflite/fp32.tflite`, `tflite/int8.tflite` | the same weights through a Keras twin, for TensorFlow Lite Micro comparisons (post-training int8 quantization) |
| `test_vectors.h` | one test image plus reference logits for an on-device self-test |

## Results

| Metric | Value |
|---|---|
| MNIST test accuracy (fp32) | 97.46 % |
| Parameters | 14,410 |
| Inference, RP2040 @ 133 MHz, fp32 portable kernels | 766 ms |

## Training

Standard MNIST, Adam, a few epochs on CPU; the script is `train/train_mnist.py` in the
[Pico4ML-BLE repo](https://github.com/eoinjordan/executorch-zephyr-arducam-pico4ML-BLE).
Export with `torch.export` then `to_edge_transform_and_lower(...).to_executorch()`; the
int8 variants use `CortexMQuantizer` + `prepare_pt2e`/`convert_pt2e` and the Cortex-M pass
manager from ExecuTorch 1.5.

## Where it runs

- [executorch-zephyr-arducam-pico4ML-BLE](https://github.com/eoinjordan/executorch-zephyr-arducam-pico4ML-BLE): camera to screen digit reader
- [executorch-zephyr-xiao-esp32s3](https://github.com/eoinjordan/executorch-zephyr-xiao-esp32s3): same on the XIAO ESP32-S3 Sense
- [executorch-vs-tflm-benchmarks](https://github.com/eoinjordan/executorch-vs-tflm-benchmarks): ExecuTorch vs TFLM, fp32 and int8, across boards

## License

Apache-2.0. MNIST is the public dataset by LeCun, Cortes and Burges.