spikeforge model hub catalog

spikeforge model hub

16 catalog entries -- 6 with trained weights, 16 with a verified source and license. Generated from spikeforge_hub/models.json by scripts/build_hub_page.py -- not a live view of what is downloaded or cached.

6 entries carry trained weights -- checkpoints this project trained itself, shipped with spikeforge-hub, each showing what it scores on the complete held-out test split. They are reference configurations with stock hyperparameters, not tuned attempts at state of the art, and every one names the command that reproduces it. The remaining entries are bundled: this project's own NIR graph presets -- structure, with freshly-initialised weights. No third-party weights are redistributed. See CURATION.md for the verification policy, and open a pull request there to propose a real, checked entry.

Name / id	Weights	Framework	Kind	Source	License	Notes
mnist / fc_legacy (94.29%) `reference/mnist-fc-legacy`	trained weights 94.29% on 10000 held-out mnist samples	snntorch	state_dict	reference `fc_legacy`	BSD-3-Clause verified source	Trained by this project on the full mnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 94.29% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-fc-legacy. The default topology and the README quickstart's dataset.
mnist / fc_small (92.16%) `reference/mnist-fc-small`	trained weights 92.16% on 10000 held-out mnist samples	snntorch	state_dict	reference `fc_small`	BSD-3-Clause verified source	Trained by this project on the full mnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 92.16% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-fc-small. The smaller fully-connected preset, for edge-sized budgets.
mnist / conv_net (97.13%) `reference/mnist-conv-net`	trained weights 97.13% on 10000 held-out mnist samples	snntorch	state_dict	reference `conv_net`	BSD-3-Clause verified source	Trained by this project on the full mnist training split: 2 epochs, 25 time steps, seed 0, CPU. Scores 97.13% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-conv-net. Convolutional LIF; fewer epochs because each one costs more.
mnist / recurrent_net (92.46%) `reference/mnist-recurrent-net`	trained weights 92.46% on 10000 held-out mnist samples	snntorch	state_dict	reference `recurrent_net`	BSD-3-Clause verified source	Trained by this project on the full mnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 92.46% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-recurrent-net. Recurrent LIF with a one-step delayed feedback edge.
fashion / fc_legacy (75.55%) `reference/fashion-fc-legacy`	trained weights 75.55% on 10000 held-out fashion samples	snntorch	state_dict	reference `fc_legacy`	BSD-3-Clause verified source	Trained by this project on the full fashion training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 75.55% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only fashion-fc-legacy. Fashion-MNIST: same shape, a harder ten-class problem.
kmnist / fc_legacy (70.79%) `reference/kmnist-fc-legacy`	trained weights 70.79% on 10000 held-out kmnist samples	snntorch	state_dict	reference `fc_legacy`	BSD-3-Clause verified source	Trained by this project on the full kmnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 70.79% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only kmnist-fc-legacy. Kuzushiji-MNIST: cursive Japanese characters.
Legacy FC LIF (fc_legacy) `nir/fc_legacy`	structure only	nir	nir_graph	bundled `fc_legacy`	BSD-3-Clause verified source	Shipped fc_legacy preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.
Small FC LIF (fc_small) `nir/fc_small`	structure only	nir	nir_graph	bundled `fc_small`	BSD-3-Clause verified source	Shipped fc_small preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.
Convolutional LIF (conv_net) `nir/conv_net`	structure only	nir	nir_graph	bundled `conv_net`	BSD-3-Clause verified source	Shipped conv_net preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.
Recurrent LIF (recurrent_net) `nir/recurrent_net`	structure only	nir	nir_graph	bundled `recurrent_net`	BSD-3-Clause verified source	Shipped recurrent_net preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.
snnTorch FC reference `snntorch/fc_reference`	structure only	snntorch	nir_graph	bundled `fc_small`	BSD-3-Clause verified source	Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped fc_small preset, so a snnTorch-authored model can be compared against a known-good structure.
snnTorch recurrent reference `snntorch/recurrent_reference`	structure only	snntorch	nir_graph	bundled `recurrent_net`	BSD-3-Clause verified source	Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped recurrent_net preset.
SpikingJelly conv reference `spikingjelly/conv_reference`	structure only	spikingjelly	nir_graph	bundled `conv_net`	BSD-3-Clause verified source	Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped conv_net preset.
SpikingJelly FC reference `spikingjelly/fc_reference`	structure only	spikingjelly	nir_graph	bundled `fc_small`	BSD-3-Clause verified source	Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped fc_small preset.
Norse conv reference `norse/conv_reference`	structure only	norse	nir_graph	bundled `conv_net`	BSD-3-Clause verified source	Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped conv_net preset, used as the first simulator-backend reference target.
Lava/Loihi recurrent reference `lava/recurrent_reference`	structure only	lava	nir_graph	bundled `recurrent_net`	BSD-3-Clause verified source	Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped recurrent_net preset, used as a declared hardware-path reference.

Name / id

Weights

Framework

Kind

Source

License

Notes

mnist / fc_legacy (94.29%)
reference/mnist-fc-legacy

trained weights
94.29% on 10000 held-out mnist samples

snntorch

state_dict

reference
fc_legacy

BSD-3-Clause verified source

Trained by this project on the full mnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 94.29% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-fc-legacy. The default topology and the README quickstart's dataset.

mnist / fc_small (92.16%)
reference/mnist-fc-small

trained weights
92.16% on 10000 held-out mnist samples

snntorch

state_dict

reference
fc_small

BSD-3-Clause verified source

Trained by this project on the full mnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 92.16% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-fc-small. The smaller fully-connected preset, for edge-sized budgets.

mnist / conv_net (97.13%)
reference/mnist-conv-net

trained weights
97.13% on 10000 held-out mnist samples

snntorch

state_dict

reference
conv_net

BSD-3-Clause verified source

Trained by this project on the full mnist training split: 2 epochs, 25 time steps, seed 0, CPU. Scores 97.13% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-conv-net. Convolutional LIF; fewer epochs because each one costs more.

mnist / recurrent_net (92.46%)
reference/mnist-recurrent-net

trained weights
92.46% on 10000 held-out mnist samples

snntorch

state_dict

reference
recurrent_net

BSD-3-Clause verified source

Trained by this project on the full mnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 92.46% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only mnist-recurrent-net. Recurrent LIF with a one-step delayed feedback edge.

fashion / fc_legacy (75.55%)
reference/fashion-fc-legacy

trained weights
75.55% on 10000 held-out fashion samples

snntorch

state_dict

reference
fc_legacy

BSD-3-Clause verified source

Trained by this project on the full fashion training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 75.55% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only fashion-fc-legacy. Fashion-MNIST: same shape, a harder ten-class problem.

kmnist / fc_legacy (70.79%)
reference/kmnist-fc-legacy

trained weights
70.79% on 10000 held-out kmnist samples

snntorch

state_dict

reference
fc_legacy

BSD-3-Clause verified source

Trained by this project on the full kmnist training split: 3 epochs, 25 time steps, seed 0, CPU. Scores 70.79% on all 10000 held-out samples. A reference configuration with stock hyperparameters, not a tuned attempt at state of the art. Reproduce with: python scripts/train_reference_models.py --only kmnist-fc-legacy. Kuzushiji-MNIST: cursive Japanese characters.

Legacy FC LIF (fc_legacy)
nir/fc_legacy

structure only

nir

nir_graph

bundled
fc_legacy

BSD-3-Clause verified source

Shipped fc_legacy preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.

Small FC LIF (fc_small)
nir/fc_small

structure only

nir

nir_graph

bundled
fc_small

BSD-3-Clause verified source

Shipped fc_small preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.

Convolutional LIF (conv_net)
nir/conv_net

structure only

nir

nir_graph

bundled
conv_net

BSD-3-Clause verified source

Shipped conv_net preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.

Recurrent LIF (recurrent_net)
nir/recurrent_net

structure only

nir

nir_graph

bundled
recurrent_net

BSD-3-Clause verified source

Shipped recurrent_net preset rendered as a NIR graph. Bundled with the project; no third-party weights are redistributed.

snnTorch FC reference
snntorch/fc_reference

structure only

snntorch

nir_graph

bundled
fc_small

BSD-3-Clause verified source

Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped fc_small preset, so a snnTorch-authored model can be compared against a known-good structure.

snnTorch recurrent reference
snntorch/recurrent_reference

structure only

snntorch

nir_graph

bundled
recurrent_net

BSD-3-Clause verified source

Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped recurrent_net preset.

SpikingJelly conv reference
spikingjelly/conv_reference

structure only

spikingjelly

nir_graph

bundled
conv_net

BSD-3-Clause verified source

Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped conv_net preset.

SpikingJelly FC reference
spikingjelly/fc_reference

structure only

spikingjelly

nir_graph

bundled
fc_small

BSD-3-Clause verified source

Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped fc_small preset.

Norse conv reference
norse/conv_reference

structure only

norse

nir_graph

bundled
conv_net

BSD-3-Clause verified source

Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped conv_net preset, used as the first simulator-backend reference target.

Lava/Loihi recurrent reference
lava/recurrent_reference

structure only

lava

nir_graph

bundled
recurrent_net

BSD-3-Clause verified source

Ecosystem provenance only: the artifact is the bundled NIR graph derived from the shipped recurrent_net preset, used as a declared hardware-path reference.