---
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

```text
{cifar100,imagenet200}/{hybrid,physical}/{tiny,mid,small}/model.pt
```

## Loading

```python
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.