This checkpoint segments medieval manuscript pages into eight
semantic classes. It is a multi-class adaptation of MapSAM (Xia et al. 2024).
Important: base model required
model.pth is not a standalone SAM checkpoint. It contains the trained rank-8
DoRA parameters, four depthwise self-prompt-generator branches, prompt encoder,
and eight-class mask decoder. Inference also requires Meta's original SAM ViT-L
checkpoint, sam_vit_l_0b3195.pth, obtained separately under its own terms.
Classes
ID
Label
0
unlabeled
1
line filler
2
marginal images
3
painted initials
4
miniatures
5
borders
6
pen flourishing
7
pen flourished texts
Evaluation
The checkpoint was evaluated on 154 held-out manuscript pages after retiling
764 model-input tiles to page resolution.
Metric
Value
Overall pixel accuracy
0.953899
Foreground pixel accuracy
0.702386
Foreground mean IoU (classes 1–7)
0.552400
Marginal-images IoU
0.460426
Marginal-images Dice
0.630537
Class
IoU
Dice
unlabeled
0.967716
0.983593
line filler
0.665873
0.799428
marginal images
0.460426
0.630537
painted initials
0.525954
0.689345
miniatures
0.618410
0.764220
borders
0.553674
0.712729
pen flourishing
0.507451
0.673257
pen flourished texts
0.535014
0.697080
Raw and normalized confusion matrix
Full counts, precision, recall, and the confusion matrix are in
evaluation/metrics.json.
Loading and inference
Clone the source revision above, install its dependencies, download the base
SAM ViT-L checkpoint, and download this repository's files. From the source
checkout, inference follows this pattern:
config.txt contains only inference-relevant settings and intentionally omits
the machine-local paths recorded by the training job. config.json provides
the same architecture metadata in a structured form.
Training summary
Backbone: SAM ViT-L
Input size: 1024 × 1024
Output resolution: 256 × 256 before page-space resizing
DoRA rank: 8, applied to image-encoder query and value projections
Self-prompt generator: depthwise variant
Optimizer: AdamW
Base learning rate: 0.0001 with 750-iteration warmup
Batch size: 6
Training data: 340 manuscript pages converted to 1,925 tiles
Evaluation data: 154 pages converted to 764 tiles
The test split was evaluated repeatedly during training, so the reported test
metrics should be treated as descriptive rather than as an unbiased estimate
from a never-observed final test set.
Limitations and intended use
This model was trained on a specialized collection of medieval manuscript
images. Performance may degrade for other repositories, imaging conditions,
layouts, periods, or visual traditions.
This checkpoint is released under the MIT License.
The separately obtained SAM base checkpoint remains governed by its own license.
