|
--- |
|
license: agpl-3.0 |
|
base_model: |
|
- Ultralytics/YOLOv8 |
|
pipeline_tag: object-detection |
|
datasets: |
|
- tech4humans/signature-detection |
|
metrics: |
|
- f1 |
|
- precision |
|
- recall |
|
library_name: ultralytics |
|
library_version: 8.0.239 |
|
inference: false |
|
tags: |
|
- object-detection |
|
- signature-detection |
|
- yolo |
|
- yolov8 |
|
- pytorch |
|
model-index: |
|
- name: tech4humans/yolov8s-signature-detector |
|
results: |
|
- task: |
|
type: object-detection |
|
dataset: |
|
type: tech4humans/signature-detection |
|
name: tech4humans/signature-detection |
|
split: test |
|
metrics: |
|
- type: precision |
|
value: 0.94499 |
|
name: [email protected] |
|
- type: precision |
|
value: 0.6735 |
|
name: [email protected]:0.95 |
|
- type: precision |
|
value: 0.947396 |
|
name: precision |
|
- type: recall |
|
value: 0.897216 |
|
name: recall |
|
- type: f1 |
|
value: 0.921623 |
|
--- |
|
|
|
# **YOLOv8s - Handwritten Signature Detection** |
|
|
|
This repository presents a YOLOv8s-based model, fine-tuned to detect handwritten signatures in document images. |
|
|
|
| Resource | Links / Badges | Details | |
|
|---------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| **Article** | [](https://huggingface.co/blog/samuellimabraz/signature-detection-model) | A detailed community article covering the full development process of the project | |
|
| **Model Files** | [](https://huggingface.co/tech4humans/yolov8s-signature-detector) | **Available formats:** [](https://pytorch.org/) [](https://onnx.ai/) [](https://developer.nvidia.com/tensorrt) | |
|
| **Dataset – Original** | [](https://universe.roboflow.com/tech-ysdkk/signature-detection-hlx8j) | 2,819 document images annotated with signature coordinates | |
|
| **Dataset – Processed** | [](https://huggingface.co/datasets/tech4humans/signature-detection) | Augmented and pre-processed version (640px) for model training | |
|
| **Notebooks – Model Experiments** | [](https://colab.research.google.com/drive/1wSySw_zwyuv6XSaGmkngI4dwbj-hR4ix) [](https://api.wandb.ai/links/samuel-lima-tech4humans/30cmrkp8) | Complete training and evaluation pipeline with selection among different architectures (yolo, detr, rt-detr, conditional-detr, yolos) | |
|
| **Notebooks – HP Tuning** | [](https://colab.research.google.com/drive/1wSySw_zwyuv6XSaGmkngI4dwbj-hR4ix) [](https://api.wandb.ai/links/samuel-lima-tech4humans/31a6zhb1) | Optuna trials for optimizing the precision/recall balance | |
|
| **Inference Server** | [](https://github.com/tech4ai/t4ai-signature-detect-server) | Complete deployment and inference pipeline with Triton Inference Server<br> [](https://docs.openvino.ai/2025/index.html) [](https://www.docker.com/) [](https://developer.nvidia.com/triton-inference-server) | |
|
| **Live Demo** | [](https://huggingface.co/spaces/tech4humans/signature-detection) | Graphical interface with real-time inference<br> [](https://www.gradio.app/) [](https://plotly.com/python/) | |
|
|
|
--- |
|
|
|
## **Dataset** |
|
|
|
<table> |
|
<tr> |
|
<td style="text-align: center; padding: 10px;"> |
|
<a href="https://universe.roboflow.com/tech-ysdkk/signature-detection-hlx8j"> |
|
<img src="https://app.roboflow.com/images/download-dataset-badge.svg"> |
|
</a> |
|
</td> |
|
<td style="text-align: center; padding: 10px;"> |
|
<a href="https://huggingface.co/datasets/tech4humans/signature-detection"> |
|
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md-dark.svg" alt="Dataset on HF"> |
|
</a> |
|
</td> |
|
</tr> |
|
</table> |
|
|
|
The training utilized a dataset built from two public datasets: [Tobacco800](https://paperswithcode.com/dataset/tobacco-800) and [signatures-xc8up](https://universe.roboflow.com/roboflow-100/signatures-xc8up), unified and processed in [Roboflow](https://roboflow.com/). |
|
|
|
**Dataset Summary:** |
|
- Training: 1,980 images (70%) |
|
- Validation: 420 images (15%) |
|
- Testing: 419 images (15%) |
|
- Format: COCO JSON |
|
- Resolution: 640x640 pixels |
|
|
|
 |
|
|
|
--- |
|
|
|
## **Training Process** |
|
|
|
The training process involved the following steps: |
|
|
|
### 1. **Model Selection:** |
|
|
|
Various object detection models were evaluated to identify the best balance between precision, recall, and inference time. |
|
|
|
|
|
| **Metric** | [rtdetr-l](https://github.com/ultralytics/assets/releases/download/v8.2.0/rtdetr-l.pt) | [yolos-base](https://huggingface.co/hustvl/yolos-base) | [yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) | [conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) | [detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) | [yolov8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | [yolov8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | [yolov8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | [yolov8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | [yolov8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | [yolo11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | [yolo11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | [yolo11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | [yolo11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | [yolo11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | [yolov10x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10x.pt) | [yolov10l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10l.pt) | [yolov10b](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10b.pt) | [yolov10m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10m.pt) | [yolov10s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10s.pt) | [yolov10n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov10n.pt) | |
|
|:---------------------|---------:|-----------:|-----------:|---------------------------:|---------------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:| |
|
| **Inference Time - CPU (ms)** | 583.608 | 1706.49 | 265.346 | 476.831 | 425.649 | 1259.47 | 871.329 | 401.183 | 216.6 | 110.442 | 1016.68 | 518.147 | 381.652 | 179.792 | 106.656 | 821.183 | 580.767 | 473.109 | 320.12 | 150.076 | **73.8596** | |
|
| **mAP50** | 0.92709 | 0.901154 | 0.869814 | **0.936524** | 0.88885 | 0.794237| 0.800312| 0.875322| 0.874721| 0.816089| 0.667074| 0.707409| 0.809557| 0.835605| 0.813799| 0.681023| 0.726802| 0.789835| 0.787688| 0.663877| 0.734332 | |
|
| **mAP50-95** | 0.622364 | 0.583569 | 0.469064 | 0.653321 | 0.579428 | 0.552919| 0.593976| **0.665495**| 0.65457 | 0.623963| 0.482289| 0.499126| 0.600797| 0.638849| 0.617496| 0.474535| 0.522654| 0.578874| 0.581259| 0.473857| 0.552704 | |
|
|
|
|
|
 |
|
|
|
#### Highlights: |
|
- **Best mAP50:** `conditional-detr-resnet-50` (**0.936524**) |
|
- **Best mAP50-95:** `yolov8m` (**0.665495**) |
|
- **Fastest Inference Time:** `yolov10n` (**73.8596 ms**) |
|
|
|
Detailed experiments are available on [**Weights & Biases**](https://api.wandb.ai/links/samuel-lima-tech4humans/30cmrkp8). |
|
|
|
### 2. **Hyperparameter Tuning:** |
|
|
|
The YOLOv8s model, which demonstrated a good balance of inference time, precision, and recall, was selected for hyperparameter tuning. |
|
|
|
[Optuna](https://optuna.org/) was used for 20 optimization trials. |
|
The hyperparameter tuning used the following parameter configuration: |
|
|
|
```python |
|
dropout = trial.suggest_float("dropout", 0.0, 0.5, step=0.1) |
|
lr0 = trial.suggest_float("lr0", 1e-5, 1e-1, log=True) |
|
box = trial.suggest_float("box", 3.0, 7.0, step=1.0) |
|
cls = trial.suggest_float("cls", 0.5, 1.5, step=0.2) |
|
opt = trial.suggest_categorical("optimizer", ["AdamW", "RMSProp"]) |
|
``` |
|
|
|
Results can be visualized here: [**Hypertuning Experiment**](https://api.wandb.ai/links/samuel-lima-tech4humans/31a6zhb1). |
|
|
|
 |
|
|
|
### 3. **Evaluation:** |
|
|
|
The models were evaluated on the test set at the end of training in ONNX (CPU) and TensorRT (GPU - T4) formats. Performance metrics included precision, recall, mAP50, and mAP50-95. |
|
|
|
 |
|
|
|
#### Results Comparison: |
|
|
|
| Metric | Base Model | Best Trial (#10) | Difference | |
|
|------------|------------|-------------------|-------------| |
|
| mAP50 | 87.47% | **95.75%** | +8.28% | |
|
| mAP50-95 | 65.46% | **66.26%** | +0.81% | |
|
| Precision | **97.23%** | 95.61% | -1.63% | |
|
| Recall | 76.16% | **91.21%** | +15.05% | |
|
| F1-score | 85.42% | **93.36%** | +7.94% | |
|
|
|
--- |
|
|
|
## **Results** |
|
|
|
After hyperparameter tuning of the YOLOv8s model, the best model achieved the following results on the test set: |
|
|
|
- **Precision:** 94.74% |
|
- **Recall:** 89.72% |
|
- **mAP@50:** 94.50% |
|
- **mAP@50-95:** 67.35% |
|
- **Inference Time:** |
|
- **ONNX Runtime (CPU):** 171.56 ms |
|
- **TensorRT (GPU - T4):** 7.657 ms |
|
|
|
--- |
|
|
|
## **How to Use** |
|
|
|
The `YOLOv8s` model can be used via CLI or Python code using the [Ultralytics](https://github.com/ultralytics/ultralytics) library. Alternatively, it can be used directly with ONNX Runtime or TensorRT. |
|
|
|
The final weights are available in the main directory of the repository: |
|
- [`yolov8s.pt`](yolov8s.pt) (PyTorch format) |
|
- [`yolov8s.onnx`](yolov8s.onnx) (ONNX format) |
|
- [`yolov8s.engine`](yolov8s.engine) (TensorRT format) |
|
|
|
### Python Code |
|
|
|
- Dependencies |
|
|
|
```bash |
|
pip install ultralytics supervision huggingface_hub |
|
``` |
|
|
|
- Inference |
|
|
|
```python |
|
import cv2 |
|
import supervision as sv |
|
|
|
from huggingface_hub import hf_hub_download |
|
from ultralytics import YOLO |
|
|
|
model_path = hf_hub_download( |
|
repo_id="tech4humans/yolov8s-signature-detector", |
|
filename="yolov8s.pt" |
|
) |
|
|
|
model = YOLO(model_path) |
|
|
|
image_path = "/path/to/your/image.jpg" |
|
image = cv2.imread(image_path) |
|
|
|
results = model(image_path) |
|
|
|
detections = sv.Detections.from_ultralytics(results[0]) |
|
|
|
box_annotator = sv.BoxAnnotator() |
|
annotated_image = box_annotator.annotate(scene=image, detections=detections) |
|
|
|
cv2.imshow("Detections", annotated_image) |
|
cv2.waitKey(0) |
|
cv2.destroyAllWindows() |
|
``` |
|
|
|
Ensure the paths to the image and model files are correct. |
|
|
|
|
|
### CLI |
|
|
|
- Dependencies |
|
|
|
```bash |
|
pip install -U ultralytics "huggingface_hub[cli]" |
|
``` |
|
|
|
- Inference |
|
|
|
```bash |
|
huggingface-cli download tech4humans/yolov8s-signature-detector yolov8s.pt |
|
``` |
|
|
|
```bash |
|
yolo predict model=yolov8s.pt source=caminho/para/imagem.jpg |
|
``` |
|
|
|
**Parameters**: |
|
- `model`: Path to the model weights file. |
|
- `source`: Path to the image or directory of images for detection. |
|
|
|
### ONNX Runtime |
|
|
|
For optimized inference, you can find the inference code using [onnxruntime](https://onnxruntime.ai/docs/) and [OpenVINO Execution Provider](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html) in the [handler.py](handler.py) file and on the Hugging Face Space [here](https://huggingface.co/spaces/tech4humans/signature-detection). |
|
|
|
--- |
|
|
|
## **Demo** |
|
|
|
You can explore the model and test real-time inference in the Hugging Face Spaces demo, built with Gradio and ONNXRuntime. |
|
|
|
[](https://huggingface.co/spaces/tech4humans/signature-detection) |
|
|
|
--- |
|
|
|
## 🔗 **Inference with Triton Server** |
|
|
|
If you want to deploy this signature detection model in a production environment, check out our inference server repository based on the NVIDIA Triton Inference Server. |
|
|
|
<table> |
|
<tr> |
|
<td> |
|
<a href="https://github.com/triton-inference-server/server"><img src="https://img.shields.io/badge/Triton-Inference%20Server-76B900?style=for-the-badge&labelColor=black&logo=nvidia" alt="Triton Badge" /></a> |
|
</td> |
|
<td> |
|
<a href="https://github.com/tech4ai/t4ai-signature-detect-server"><img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white" alt="GitHub Badge" /></a> |
|
</td> |
|
</tr> |
|
</table> |
|
|
|
--- |
|
|
|
## **Infrastructure** |
|
|
|
### Software |
|
|
|
The model was trained and tuned using a Jupyter Notebook environment. |
|
|
|
- **Operating System:** Ubuntu 22.04 |
|
- **Python:** 3.10.12 |
|
- **PyTorch:** 2.5.1+cu121 |
|
- **Ultralytics:** 8.3.58 |
|
- **Roboflow:** 1.1.50 |
|
- **Optuna:** 4.1.0 |
|
- **ONNX Runtime:** 1.20.1 |
|
- **TensorRT:** 10.7.0 |
|
|
|
### Hardware |
|
|
|
Training was performed on a Google Cloud Platform n1-standard-8 instance with the following specifications: |
|
|
|
- **CPU:** 8 vCPUs |
|
- **GPU:** NVIDIA Tesla T4 |
|
|
|
--- |
|
|
|
## **License** |
|
|
|
### Model Weights (Fine-Tuned Model) – **AGPL-3.0** |
|
- **License:** GNU Affero General Public License v3.0 (AGPL-3.0) |
|
- **Usage:** The fine-tuned model weights, derived from the YOLOv8 model by Ultralytics, are licensed under AGPL-3.0. This requires that any modifications or derivative works of these model weights also be distributed under AGPL-3.0, and if the model is used as part of a network service, the corresponding source must be made available. |
|
|
|
### Code, Training, Deployment, and Data – **Apache 2.0** |
|
- **License:** Apache License 2.0 |
|
- **Usage:** All additional materials—including training scripts, deployment code, usage instructions, and associated data—are licensed under the Apache 2.0 license. |
|
|
|
For more details, please refer to the full license texts: |
|
- [GNU AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html) |
|
- [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
|
|
|
--- |
|
|
|
## **Contact and Information** |
|
|
|
For further information, questions, or contributions, contact us at **[email protected]**. |
|
|
|
<div align="center"> |
|
<p> |
|
📧 <b>Email:</b> <a href="mailto:[email protected]">[email protected]</a><br> |
|
🌐 <b>Website:</b> <a href="https://www.tech4.ai/">www.tech4.ai</a><br> |
|
💼 <b>LinkedIn:</b> <a href="https://www.linkedin.com/company/tech4humans-hyperautomation/">Tech4Humans</a> |
|
</p> |
|
</div> |
|
|
|
## **Author** |
|
|
|
<div align="center"> |
|
<table> |
|
<tr> |
|
<td align="center" width="140"> |
|
<a href="https://huggingface.co/samuellimabraz"> |
|
<img src="https://avatars.githubusercontent.com/u/115582014?s=400&u=c149baf46c51fdee45ad5344cf1b360236d90d09&v=4" width="120" alt="Samuel Lima"/> |
|
<h3>Samuel Lima</h3> |
|
</a> |
|
<p><i>AI Research Engineer</i></p> |
|
<p> |
|
<a href="https://huggingface.co/samuellimabraz"> |
|
<img src="https://img.shields.io/badge/🤗_HuggingFace-samuellimabraz-orange" alt="HuggingFace"/> |
|
</a> |
|
</p> |
|
</td> |
|
<td width="500"> |
|
<h4>Responsibilities in this Project</h4> |
|
<ul> |
|
<li>🔬 Model development and training</li> |
|
<li>📊 Dataset analysis and processing</li> |
|
<li>⚙️ Hyperparameter optimization and performance evaluation</li> |
|
<li>📝 Technical documentation and model card</li> |
|
</ul> |
|
</td> |
|
</tr> |
|
</table> |
|
</div> |
|
|
|
--- |
|
|
|
<div align="center"> |
|
<p>Developed with 💜 by <a href="https://www.tech4.ai/">Tech4Humans</a></p> |
|
</div> |
