ModelPublicadoExterno

Invoice Layout Yolo11x

Fine-tuned YOLO11x for 8-class invoice document layout region detection (metadata, vendor/customer blocks, table, line items, summary, payment, column headers).

Fuentehuggingface.co/AvoCahDoe/invoice-layout-yolo11x/tree/0cdd48f419c7e775c73455c06a932c36f7374814

TipoOtherModelo baseultralytics/yolo11xVersiónRevision 0cdd48fBcalidad

Fuente del modelo

Tipo: Other
Tamaño del archivo: 109 MB
Modelo base: ultralytics/yolo11x
Versión: vRevision 0cdd48f
Actualizado: 16 sept 2026
Descargas: 75
Fuente: Hugging Face

Descripción de la fuente

Invoice Layout Detection — YOLO11x

Fine-tuned YOLO11x 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	YOLO11x (56.9M params, 194.9 GFLOPs)
Base weights	`ultralytics/yolo11x`
Input size	1024 px
Classes	8 layout regions
Training epochs	100 (best @ epoch 85)
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	0.8738
mAP50-95	0.6127
Precision	0.9484
Recall	0.8530

Validation (best epoch 85)

Metric	Best	Final (epoch 100)
mAP50	0.8944	0.8803
mAP50-95	0.6142	0.5848
Precision	0.8898	0.9025
Recall	0.7935	0.7559

Training time: ~76.5 min on RTX 4070 Laptop GPU.

Training configuration

Parameter	Value
Batch	2
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-yolo11x", "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-yolo11x/weights/best.pt")
results = model.predict("invoice_page.png")

Training reproduction

python scripts/train_comparison.py --models yolo11x

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` ← this model	0.8738	0.6127	0.9484	0.8530	YOLO11x
5	`yolo11m`	0.8418	0.4926	0.9033	0.8466	YOLO11m
6	`yolov8m`	0.8388	0.5289	0.9077	0.8580	YOLOv8m

License

Apache 2.0

Fuentes

1 fuente

Hugging FacePrincipalVersión 0cdd48f419c7e775c73455c06a932c36f7374814

huggingface.co/AvoCahDoe/invoice-layout-yolo11x/tree/0cdd48f419c7e775c73455c06a932c36f7374814

Verificado 1 sept

Artefactos del modelo

3 artefactos

best.pt

pt · 109 MB · SHA-256 6b3fe8ac4d75…797b · Hugging Face

Descargar

weights/best.pt

pt · 109 MB · SHA-256 6b3fe8ac4d75…797b · Hugging Face

Descargar

weights/last.pt

pt · 109 MB · SHA-256 6d7f5b59d46b…3bb4 · Hugging Face

Descargar

Extractos de fuentes

2 extractos

Readme1 septhuggingface.co/AvoCahDoe/invoice-layout-yolo11x

--- 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/yolo11x 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: 0.8738 - type: map value: 0.6127 - type: precision value: 0.9484 - type: recall value: 0.8530 --- # Invoice Layout Detection — YOLO11x Fine-tuned **YOLO11x** 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** | YOLO11x (56.9M params, 194.9 GFLOPs) | | **Base weights** | `ultralytics/yolo11x` | | **Input size** | 1024 px | | **Classes** | 8 layout regions | | **Training epochs** | 100 (best @ epoch 85) | | **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** | 0.8738 | | **mAP50-95** | 0.6127 | | **Precision** | 0.9484 | | **Recall** | 0.8530 | ### Validation (best epoch 85) | Metric | Best | Final (epoch 100) | |--------|------|-----------------------------------------------| | mAP50 | 0.8944 | 0.8803 | | mAP50-95 | 0.6142 | 0.5848 | | Precision | 0.8898 | 0.9025 | | Recall | 0.7935 | 0.7559 | Training time: ~76.5 min on RTX 4070 Laptop GPU. ## Training configuration | Parameter | Value | |-----------|-------| | Batch |...

Note1 septhuggingface.co/AvoCahDoe/invoice-layout-yolo11x

Source context: 75 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo AvoCahDoe/invoice-layout-yolo11x