AvoCahDoe
/

invoice-layout-yolov8m

@@ -1,183 +1,32 @@
 ---
 license: apache-2.0
-language:
-  - en
-  - de
 tags:
   - object-detection
   - yolo
-  - yolov8
-  - ultralytics
   - invoice
   - document-layout
-  - document-understanding
-  - ocr-prep
 library_name: ultralytics
-pipeline_tag: object-detection
-base_model: ultralytics/yolov8m
-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: 0.8388
-          - type: map
-            value: 0.5289
-          - type: precision
-            value: 0.9077
-          - type: recall
-            value: 0.8580
 ---
-# Invoice Layout Detection — YOLOv8m
-Fine-tuned **YOLOv8m** 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** | YOLOv8m (25.9M params, 78.9 GFLOPs) |
-| **Base weights** | `ultralytics/yolov8m` |
-| **Input size** | 1024 px |
-| **Classes** | 8 layout regions |
-| **Training epochs** | 100 (best @ epoch 98) |
-| **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** | 0.8388 |
-| **mAP50-95** | 0.5289 |
-| **Precision** | 0.9077 |
-| **Recall** | 0.8580 |
-### Validation (best epoch 98)
-| Metric | Best | Final (epoch 100) |
-|--------|------|-----------------------------------------------|
-| mAP50 | 0.8581 | 0.8578 |
-| mAP50-95 | 0.5554 | 0.5544 |
-| Precision | 0.8388 | 0.8253 |
-| Recall | 0.7730 | 0.7651 |
-Training time: ~66.6 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](assets/results.png)
-### Precision–Recall
-![PR curve](assets/BoxPR_curve.png)
-### Confusion matrices
-| Raw | Normalized |
-|-----|------------|
-| ![Confusion matrix](assets/confusion_matrix.png) | ![Normalized confusion matrix](assets/confusion_matrix_normalized.png) |
-### Validation predictions
-| Ground truth | Model predictions |
-|--------------|-------------------|
-| ![Val labels](assets/val_batch0_labels.jpg) | ![Val predictions](assets/val_batch0_pred.jpg) |
-### Training batches (augmented)
-| Batch 0 | Batch 1 | Batch 2 |
-|---------|---------|---------|
-| ![Train batch 0](assets/train_batch0.jpg) | ![Train batch 1](assets/train_batch1.jpg) | ![Train batch 2](assets/train_batch2.jpg) |
-### Label distribution
-![Label distribution](assets/labels.jpg)
 ## Usage
-### Ultralytics (recommended)
-```python
-from ultralytics import YOLO
-from huggingface_hub import hf_hub_download
-weights = hf_hub_download("AvoCahDoe/invoice-layout-yolov8m", "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
 ```python
 from ultralytics import YOLO
-model = YOLO("hf://AvoCahDoe/invoice-layout-yolov8m/weights/best.pt")
-results = model.predict("invoice_page.png")
 ```
-### Training reproduction
-```bash
-python scripts/train_comparison.py --models yolov8m
-```
-## 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
-```
-## License
-Apache 2.0

 ---
 license: apache-2.0
 tags:
   - object-detection
   - yolo
   - invoice
   - document-layout
 library_name: ultralytics
 ---
+# Invoice Layout — yolov8m
+Fine-tuned yolov8m for invoice macro-region detection (8 classes).
 ## Classes
+`invoice_metadata`, `vendor_block`, `customer_block`, `table_block`, `line_item`, `summary_block`, `payment_block`, `Column`
+## Metrics (test split)
 | Metric | Value |
 |--------|-------|
+| mAP50 | 0.8388 |
+| mAP50-95 | 0.5289 |
 ## Usage
 ```python
 from ultralytics import YOLO
+model = YOLO("best.pt")
+results = model.predict("invoice.png")
 ```