metadata
library_name: pytorch
tags:
- vision-transformer
- cifar-100
- imagenet-200
- in-synapse-activation
ISA Vision checkpoints
Model-only PyTorch checkpoints for the ISA vision experiments. The repository contains CIFAR-100 and ImageNet-200 checkpoints for hybrid and physical models at tiny, mid, and small scales.
Device equation
The physical operator evaluates
[ I(V,V_{th}) = I_S\frac{\operatorname{softplus}^2!\left(\frac{V-V_{th}}{2nU_T}\right)-\operatorname{softplus}^2!\left(\frac{V-V_{th}-V_D}{2nU_T}\right)}{1+V/V_{sat}}. ]
The checkpoint tensors already contain the fitted threshold and TIA parameters required by this operator.
Layout
{cifar100,imagenet200}/{hybrid,physical}/{tiny,mid,small}/model.pt
Loading
import torch
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="Viligance/ISA-Vision",
filename="cifar100/physical/small/model.pt",
)
checkpoint = torch.load(path, map_location="cpu")
model.load_state_dict(checkpoint["model"], strict=True)
model.eval()
checkpoint_manifest.json records each file's SHA256 digest, size, layer count, and evaluation metadata. release_validation.json summarizes artifact checks.