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Build a first SpikeForge experiment around one supported dataset, a compact network, and a short training run. Keep training data separate from test data, save the settings that produced the result, and report how the test score was obtained: the quick progress probe shown in the package quickstart is not a full held-out evaluation.
What this experiment can—and cannot—show
SpikeForge is a Python toolkit built on PyTorch and snnTorch for workflows that include loading image and neuromorphic event data, encoding samples as spikes, training and validating leaky integrate-and-fire (LIF) networks, and exporting or deploying models. The project describes itself as “Pre-1.0. Before trusting any number this produces, read Implications and boundaries.” Treat the workflow below as a small, repeatable experiment—not evidence of production readiness or a general performance benchmark. SpikeForge project overview
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A short run is useful for checking that data preparation, model inputs, training, and evaluation fit together. It cannot establish broad model quality by itself. In particular, a training metric is not a substitute for a test result, and a quick test probe is not necessarily a score over the complete held-out split.
The Tool Desk
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First decide whether the experiment uses ordinary images or recorded neuromorphic events. The event-dataset guide lists N-MNIST, DVS128 Gesture, CIFAR10-DVS, and Spiking Speech Commands. Access to the documented event-data path requires the optional events extra. Check that the dataset and its split are available for your chosen run before training. SpikeForge event-dataset guide
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For event recordings
The guide describes events as validated sparse (x, y, t, p) values: x and y are sensor coordinates, t is a zero-based time bin, and p indicates positive ON or negative OFF polarity. SpikeForge converts the stream into time-major frames with separate ON and OFF channels, then bridges those frames into tensors used by the simulator.
Choose topology with sensor geometry in mind. Spatial convolutional topologies are suited to 28×28-like geometry. For other sensor geometries, the guide recommends feature-input options such as fc_legacy, fc_small, or recurrent_net. Event recordings already contain spike trains, so image-oriented rate, latency, delta, and random coding controls do not apply to them.
Check the split before using a dataset
For the documented implementation, CIFAR10-DVS has a training pool but no declared held-out split. The guide says this results in an explicit split error rather than an evaluation that quietly reuses training examples. Do not use it to claim held-out accuracy in this workflow. The guide also distinguishes generated synthetic event streams from real recordings: synthetic streams are offline fixtures, and their accuracy is only a smoke test.
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For image datasets
Use an image dataset supported by the package workflow, then choose an image-to-spike encoding appropriate to the experiment. Keep the conversion settings in your run record. Do not apply those image coding controls to event recordings, which are already represented as spikes.
Set up a small, rerunnable run
Install SpikeForge using the package instructions and select the CPU-wheel setup path or the alternative setup described in the quickstart. The package page estimates approximately 1.1 GB for the CPU-wheel path and approximately 5.5 GB for the alternative; these are maintainer-provided setup estimates, not independent measurements. Event-data users also need the optional events extra for the documented event path. SpikeForge package quickstart
Keep the first experiment deliberately modest: use a compact model and a short epoch schedule. Before fitting, split the data so that test examples cannot influence model updates. Save the configuration alongside the output. A useful record includes the dataset and split, event conversion or image encoding settings, random seed, model name, epoch count, and exact package versions. That record makes reruns interpretable: when an outcome changes, you can see which input or setting changed.
The title-matched walkthrough demonstrates the general sequence of loading data, converting samples to events, splitting before training, and using a compact network with few epochs. Follow its code and current package instructions for the precise API, since the steps here describe the experimental design rather than reproduce version-sensitive commands. SpikeForge small-classifier walkthrough
Train, then evaluate without overstating the result
- Load and prepare the selected data. Confirm whether it is image data or an event recording and apply only the relevant conversion path.
- Separate training and test data before updates. Use the dataset’s declared held-out split where available; do not report a training score as test performance.
- Fit the compact LIF model for the planned short schedule. Keep the run configuration and output together.
- Report training and test output with the evaluation method. State whether the test value covers the full held-out split or is only a progress probe.
The package quickstart’s displayed test_accuracy is expressly a fast progress probe, not a score over the complete test split. Its example reports accuracy in the mid-80s, but the run does not set a seed and the exact result varies. That figure is therefore neither an expected outcome nor a full held-out benchmark. SpikeForge package quickstart
Best Value
Likewise, a synthetic-stream result answers only whether a smoke-test path runs; it does not establish accuracy on real event recordings. Describe the dataset, split, and evaluation method next to every reported number so readers can tell what the number actually measures.
What to record with the result
- Dataset name and whether the examples are images, real event recordings, or synthetic fixtures.
- How the data was split, and whether the reported test evaluation uses the complete held-out split or a quick probe.
- Event conversion or image encoding settings, where applicable.
- Random seed, model topology, epoch count, and exact package versions.
- Training output and test output, clearly labeled rather than blended into one claim.
This level of detail keeps a small experiment useful: another run can change one factor at a time without confusing a data, model, or evaluation change for a training improvement.
Quick Recap
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