ModelListedExternal

Invoice Layout Yolo11m

Fine-tuned YOLO11m for 8-class invoice document layout region detection (metadata, vendor/customer blocks, table, line items, summary, payment, column headers).

Sourcehuggingface.co/AvoCahDoe/invoice-layout-yolo11m/tree/985422b00cfdb1c3a516ae360a9235a51fcef06e

KindOtherBase modelultralytics/yolo11mVersionRevision 985422bBgrade

Model source

Kind: Other
File size: 38.7 MB
Base model: ultralytics/yolo11m
Version: vRevision 985422b
Updated: Sep 16, 2026
Downloads: 17
Source: Hugging Face

Source description

Invoice Layout Detection — YOLO11m

Fine-tuned YOLO11m for 8-class invoice document layout region detection (metadata, vendor/customer blocks, table, line items, summary, payment, column headers).

Part of the Hub collection Invoice Layout Extraction.

Property	Value
Architecture	YOLO11m (20.1M params, 68.0 GFLOPs)
Base weights	`ultralytics/yolo11m`
Input size	1024 px
Classes	8 layout regions
Training epochs	100 (best @ epoch 91)
Optimizer	AdamW
Dataset	AvoCahDoe/invoice-annotated-bbox
Demo Space	AvoCahDoe/invoice-layout-yolov8n-demo
Train / val / test	372 / 14 / 7 pages

Classes

ID	Name
0	`invoice_metadata`
1	`vendor_block`
2	`customer_block`
3	`table_block`
4	`line_item`
5	`summary_block`
6	`payment_block`
7	`Column`

Metrics

Test split (held-out, 7 images)

Metric	Value
mAP50	0.8418
mAP50-95	0.4926
Precision	0.9033
Recall	0.8466

Validation (best epoch 91)

Metric	Best	Final (epoch 100)
mAP50	0.8493	0.8780
mAP50-95	0.4951	0.4917
Precision	0.8280	0.8682
Recall	0.7991	0.7980

Training time: ~180.4 min on RTX 4070 Laptop GPU.

Training configuration

Parameter	Value
Batch	6
Image size	1024
Patience	25
LR (cosine)	0.01 → 0.01
Mosaic	1.0
Mixup	0.15
Copy-paste	0.1
Horizontal flip	0.0

Training results

Learning curves

Training results

Precision–Recall

PR curve

Confusion matrices

Raw	Normalized
Confusion matrix	Normalized confusion matrix

Validation predictions

Ground truth	Model predictions
Val labels	Val predictions

Training batches (augmented)

Batch 0	Batch 1	Batch 2
Train batch 0	Train batch 1	Train batch 2

Label distribution

Usage

Ultralytics (recommended)

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

weights = hf_hub_download("AvoCahDoe/invoice-layout-yolo11m", "weights/best.pt")
model = YOLO(weights)
results = model.predict("invoice_page.png", imgsz=1024, conf=0.25)
results[0].show()

Load from Hub by repo id

from ultralytics import YOLO

model = YOLO("hf://AvoCahDoe/invoice-layout-yolo11m/weights/best.pt")
results = model.predict("invoice_page.png")

Training reproduction

python scripts/train_comparison.py --models yolo11m

Repository layout

weights/best.pt          # Best checkpoint (use this)
weights/last.pt          # Last epoch checkpoint
config/                  # Training configuration
metrics/                 # Per-epoch and summary metrics
assets/                  # Plots and visualizations

Model comparison (test split)

All six architectures fine-tuned on the same invoice layout dataset.

Rank	Model	test mAP50	test mAP50-95	Precision	Recall	Hub
1	`yolov8n`	0.9600	0.7167	0.9502	0.9592	YOLOv8n
2	`yolov8s`	0.9006	0.6739	0.9419	0.8677	YOLOv8s
3	`yolov8x`	0.8757	0.6224	0.9144	0.8735	YOLOv8x
4	`yolo11x`	0.8738	0.6127	0.9484	0.8530	YOLO11x
5	`yolo11m` ← this model	0.8418	0.4926	0.9033	0.8466	YOLO11m
6	`yolov8m`	0.8388	0.5289	0.9077	0.8580	YOLOv8m

License

Apache 2.0

Sources

1 source

Hugging FacePrimaryVersion 985422b00cfdb1c3a516ae360a9235a51fcef06e

huggingface.co/AvoCahDoe/invoice-layout-yolo11m/tree/985422b00cfdb1c3a516ae360a9235a51fcef06e

Verified Sep 2

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Model artifacts

3 artifacts

best.pt

pt · 38.7 MB · SHA-256 9a99195b9c6b…9b57 · Hugging Face

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weights/best.pt

pt · 38.7 MB · SHA-256 9a99195b9c6b…9b57 · Hugging Face

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weights/last.pt

pt · 38.7 MB · SHA-256 11948a48917f…b99d · Hugging Face

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Source excerpts

2 excerpts

ReadmeSep 2huggingface.co/AvoCahDoe/invoice-layout-yolo11m

--- license: apache-2.0 language: - en - de tags: - object-detection - yolo - yolo11 - ultralytics - invoice - document-layout - document-understanding - ocr-prep - invoice-extraction library_name: ultralytics pipeline_tag: object-detection base_model: ultralytics/yolo11m datasets: - AvoCahDoe/invoice-annotated-bbox metrics: - map_50 - map - precision - recall model-index: - name: best results: - task: type: object-detection dataset: name: invoice-yolo-annotated type: invoice-layout metrics: - type: map_50 value: 0.8418 - type: map value: 0.4926 - type: precision value: 0.9033 - type: recall value: 0.8466 --- # Invoice Layout Detection — YOLO11m Fine-tuned **YOLO11m** for **8-class invoice document layout** region detection (metadata, vendor/customer blocks, table, line items, summary, payment, column headers). Part of the Hub collection **[Invoice Layout Extraction](https://huggingface.co/collections/AvoCahDoe/invoice-layout-extraction-6a39cd2a061a508c8049d825)**. | Property | Value | |----------|-------| | **Architecture** | YOLO11m (20.1M params, 68.0 GFLOPs) | | **Base weights** | `ultralytics/yolo11m` | | **Input size** | 1024 px | | **Classes** | 8 layout regions | | **Training epochs** | 100 (best @ epoch 91) | | **Optimizer** | AdamW | | **Dataset** | [AvoCahDoe/invoice-annotated-bbox](https://huggingface.co/datasets/AvoCahDoe/invoice-annotated-bbox) | | **Demo Space** | [AvoCahDoe/invoice-layout-yolov8n-demo](https://huggingface.co/spaces/AvoCahDoe/invoice-layout-yolov8n-demo) | | **Train / val / test** | 372 / 14 / 7 pages | ## Classes | ID | Name | |----|------| | 0 | `invoice_metadata` | | 1 | `vendor_block` | | 2 | `customer_block` | | 3 | `table_block` | | 4 | `line_item` | | 5 | `summary_block` | | 6 | `payment_block` | | 7 | `Column` | ## Metrics ### Test split (held-out, 7 images) | Metric | Value | |--------|-------| | **mAP50** | 0.8418 | | **mAP50-95** | 0.4926 | | **Precision** | 0.9033 | | **Recall** | 0.8466 | ### Validation (best epoch 91) | Metric | Best | Final (epoch 100) | |--------|------|-----------------------------------------------| | mAP50 | 0.8493 | 0.8780 | | mAP50-95 | 0.4951 | 0.4917 | | Precision | 0.8280 | 0.8682 | | Recall | 0.7991 | 0.7980 | Training time: ~180.4 min on RTX 4070 Laptop GPU. ## Training configuration | Parameter | Value | |-----------|-------| | Batch |...

NoteSep 2huggingface.co/AvoCahDoe/invoice-layout-yolo11m

Source context: 17 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo AvoCahDoe/invoice-layout-yolo11m