These models were developed at École nationale des chartes – PSL in the context of the O.D.I.L. project .
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# Usage
## Python — ONNX local
```bash
pip install onnxruntime pillow numpy
```
```python
import json
import numpy as np
import onnxruntime as ort
from PIL import Image
from pathlib import Path
run = Path("./mobilenet_v3_large")
cfg = json.loads((run / "inference_config.json").read_text())
pre = json.loads((run / "preprocess.json").read_text())
img = Image.open("page.jpg").convert("RGB").resize((pre["img_size"], pre["img_size"]))
x = np.asarray(img).astype("float32") / 255.0
x = (x - np.array(pre["mean"])) / np.array(pre["std"])
x = x.transpose(2, 0, 1)[None].astype("float32")
sess = ort.InferenceSession(str(run / "onnx/model.onnx"))
logits = sess.run(None, {cfg["input_name"]: x})[0][0]
probs = np.exp(logits - logits.max())
probs = probs / probs.sum()
p_illu = float(probs[cfg["positive_index"]])
label = cfg["positive_label"] if p_illu >= cfg["threshold"] else "non_illumination"
print(label, p_illu)
```
## Python — ONNX from Hugging Face
```bash
pip install huggingface_hub onnxruntime pillow numpy
```
```python
from huggingface_hub import snapshot_download
from pathlib import Path
repo = "lterriel/medieval-illumination-bin-classifier"
run_name = "final_mobilenetv3_large"
local_dir = Path(snapshot_download(
repo_id=repo,
allow_patterns=[
f"{run_name}/onnx/model.onnx",
f"{run_name}/preprocess.json",
f"{run_name}/inference_config.json",
],
)) / run_name
```
Then use the same ONNX code as above, replacing:
```python
run = Path("./mobilenet_v3_large")
```
with:
```python
run = local_dir
```
## Python — PyTorch / non-ONNX local
```bash
pip install torch torchvision pillow numpy
```
```python
import json
import torch
import numpy as np
from PIL import Image
from pathlib import Path
from torchvision import models
run = Path("./mobilenet_v3_large")
cfg = json.loads((run / "inference_config.json").read_text())
pre = json.loads((run / "preprocess.json").read_text())
model = models.mobilenet_v3_large(weights=None)
model.classifier[-1] = torch.nn.Linear(model.classifier[-1].in_features, 2)
model.load_state_dict(torch.load(run / "checkpoints/best.pt", map_location="cpu"))
model.eval()
img = Image.open("page.jpg").convert("RGB").resize((pre["img_size"], pre["img_size"]))
x = np.asarray(img).astype("float32") / 255.0
x = (x - np.array(pre["mean"])) / np.array(pre["std"])
x = torch.tensor(x.transpose(2, 0, 1)[None]).float()
with torch.no_grad():
logits = model(x)
probs = torch.softmax(logits, dim=1)[0]
p_illu = float(probs[cfg["positive_index"]])
label = cfg["positive_label"] if p_illu >= cfg["threshold"] else "non_illumination"
print(label, p_illu)
```
For another torchvision architecture, replace the model constructor:
- mobilenetV2
```
model = models.mobilenet_v2(weights=None)
model.classifier[-1] = torch.nn.Linear(model.classifier[-1].in_features, 2)
```
- mobilenetV2 (small)
```
model = models.mobilenet_v3_small(weights=None)
model.classifier[-1] = torch.nn.Linear(model.classifier[-1].in_features, 2)
```
## Python — PyTorch / non-ONNX from Hugging Face
```bash
pip install huggingface_hub torch torchvision pillow numpy
```
```python
from huggingface_hub import snapshot_download
from pathlib import Path
repo = "lterriel/medieval-illumination-bin-classifier"
run_name = "final_mobilenetv3_large"
run = Path(snapshot_download(
repo_id=repo,
allow_patterns=[
f"{run_name}/checkpoints/best.pt",
f"{run_name}/preprocess.json",
f"{run_name}/inference_config.json",
],
)) / run_name
```
Then use the same PyTorch code as above.
## JS (HF - ONNX)
```javascript
```
# Training tools
All models are finetuned with img-clf-framework, a training framework for binary image classification pipelines. Check the [training repository here]()
# Citation
If you use these models in your research, please cite:
```
@software{terriel_bsicle_2026,
AUTHOR = {Terriel, Lucas and Jolivet, Vincent},
TITLE = {{BSICLE}: Binary System for Illuminated Folio Classification with Lightweight Engines},
YEAR = {2026},
PUBLISHER = {Hugging Face},
INSTITUTION = {{École nationale des chartes -- PSL}},
URL = {https://huggingface.co/ENC-PSL/medieval-illumination-bin-classifier},
NOTE = {Family of lightweight binary image classification models for detecting illuminated folios in medieval manuscripts, developed in the context of the O.D.I.L. project},
LICENSE = {apache-2.0},
VERSION = {0.0.1}
}
```
# Funding
These models were developed at École nationale des chartes – PSL in the context of the O.D.I.L. project .