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You can build a small, repeatable event-driven classifier with SpikeForge by choosing a dataset with a documented held-out split, converting event samples into time-major ON/OFF frames, training a compact network, and reporting test results with the exact run settings. Keep one limit in view: SpikeForge is marked pre-1.0, and the quickstart’s displayed accuracy is a progress probe—not a full held-out benchmark.
What this experiment can—and cannot—show
SpikeForge is a Python toolkit built on PyTorch and snnTorch. Its documented workflow covers loading image and neuromorphic event datasets, encoding data into spikes, training and validating leaky integrate-and-fire (LIF) networks, and exporting or deploying models. The project page warns: “Pre-1.0. Before trusting any number this produces, read Implications and boundaries.” Treat this as a learning and repeatability exercise, not evidence of production readiness or a claim about physical-device timing. SpikeForge project overview.
A useful first run is intentionally modest: keep the model and epoch count small, separate training data from test data before any model updates, and save the configuration alongside the output. That makes a rerun interpretable: if the result changes, you can identify whether the dataset, conversion, seed, architecture, schedule, or package version changed.
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The documented event-data path requires the optional events extra. The event guide lists N-MNIST, DVS128 Gesture, CIFAR10-DVS, and Spiking Speech Commands, but the choice matters for evaluation as well as availability.
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| Dataset | Split and evaluation note | Practical implication |
|---|---|---|
| N-MNIST | The guide lists the dataset; it does not state split details in the cited description. | Check the installed version’s dataset documentation and confirm a separate held-out split before reporting test accuracy. |
| DVS128 Gesture | The guide lists the dataset; it does not state split details in the cited description. | Confirm split availability and geometry before selecting a topology. |
| Spiking Speech Commands | The guide lists the dataset; it does not state split details in the cited description. | Confirm split availability and geometry before selecting a topology. |
| CIFAR10-DVS | In the documented implementation, a training pool is available but no held-out split is declared; the guide says this causes an explicit split error. | Do not use it to report held-out accuracy in this workflow. Do not substitute training examples for a test set. |
For the dataset you choose, make sure the event extra and required dataset download are available in your environment. SpikeForge’s event guide also describes generated synthetic streams as offline fixtures, not real recordings; their accuracy is a smoke test and must not be represented as real-recording performance. See the event-dataset guide.
Understand the event representation and pick a compatible topology
The guide describes input events as validated sparse (x, y, t, p) values: x and y are sensor coordinates, t is a zero-based time bin, and p records positive ON or negative OFF polarity. SpikeForge converts the event stream into time-major frames with separate ON and OFF channels, then bridges those frames into tensors consumed by the simulator.
Choose the model according to the sensor geometry. Spatial convolutional topologies are intended for 28×28-like geometry. For other sensor geometries, the guide recommends feature-input options such as fc_legacy, fc_small, or recurrent_net. Since event recordings are already spike trains, image-oriented rate, latency, delta, and random coding controls do not apply to this input path.
Run a compact, reproducible experiment
- Install the documented event support. Use the package’s event-data setup path and install the optional
eventsextra. Confirm the dataset download completes before training. - Choose a dataset and verify its split. Use a dataset that exposes separate training and held-out test data in the documented implementation. Do not proceed with a purported test score if the dataset has no declared held-out split.
- Inspect geometry and representation. Confirm the sensor dimensions, event channels, and conversion to time-major ON/OFF frames. Select a spatial convolutional topology only for 28×28-like data; otherwise prefer one of the documented feature-input topologies.
- Split before training. Keep the test recordings out of all model updates and training-time selection. Use the designated training split for learning and reserve the held-out portion for evaluation.
- Keep the model and schedule small. Start with a compact network and a short epoch count. Save the configuration and output together so the experiment can be repeated without guessing which settings were used.
- Evaluate and label the result accurately. Report the test procedure and whether the result covers the complete held-out split. A quick progress value from the package quickstart is not a full test-set evaluation.
Record enough detail to make a rerun meaningful
Save these details beside each result:
- Dataset name, version or download source, and the train/test split used.
- Event conversion details, including any time-window or framing settings used by the run.
- Random seed, if set; if no seed is set, say so.
- Model/topology name and relevant architecture settings.
- Epoch count and other training settings that affect model updates.
- Exact SpikeForge, PyTorch, and snnTorch package versions.
- Evaluation method: whether the score covers the full held-out split or is only a progress probe.
This record is especially important when comparing reruns. The package quickstart says its example does not set a seed, its result varies, and its displayed test_accuracy is a fast progress probe rather than an evaluation over the complete test split. The quickstart’s mid-80s result is therefore neither a benchmark nor an expected outcome. It is not a substitute for a properly described held-out evaluation. SpikeForge package quickstart.
What to do when the run fails or the result is misleading
- The optional dependency or data is missing: verify that the
eventsextra is installed and that the dataset download is present. - The split operation errors: check whether the selected dataset declares a held-out split in this implementation. In particular, the documented CIFAR10-DVS path has no declared held-out split and errors rather than silently evaluating on training data.
- The model does not fit the input: check sensor geometry and choose a feature-input topology for non-28×28-like event data rather than assuming the spatial convolutional option is suitable.
- The reported number seems unusually strong or is hard to reproduce: check whether it came from synthetic fixture data, a quick progress probe, or the actual held-out split. State which evaluation produced the number and whether a random seed was set.
The title-matched walkthrough provides an example of loading data, converting samples to events, splitting before training, and using a compact network with few epochs: Build a small event-driven classifier with SpikeForge. Use it as a starting point, while documenting the settings and evaluation method for your own run.
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