File size: 18,544 Bytes
54a2135 9582471 54a2135 9582471 54a2135 9582471 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 |
---
license: apache-2.0
base_model:
- microsoft/conditional-detr-resnet-50
pipeline_tag: object-detection
datasets:
- tech4humans/signature-detection
metrics:
- f1
- precision
- recall
library_name: transformers
inference: false
tags:
- object-detection
- signature-detection
- detr
- conditional-detr
- pytorch
model-index:
- name: tech4humans/conditional-detr-50-signature-detector
results:
- task:
type: object-detection
dataset:
type: tech4humans/signature-detection
name: tech4humans/signature-detection
split: test
metrics:
- type: precision
value: 0.936524
name: [email protected]
- type: precision
value: 0.653321
name: [email protected]:0.95
---
# **Conditional-DETR ResNet-50 - Handwritten Signature Detection**
This repository presents a Conditional-DETR model with ResNet-50 backbone, fine-tuned to detect handwritten signatures in document images. This model achieved the **highest [email protected] (93.65%)** among all tested architectures in our comprehensive evaluation.
| 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 (YOLOv8s)** | [](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**
### **Installation**
```bash
pip install transformers torch torchvision pillow
```
### **Inference**
```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import torch
# Load model and processor
model_name = "tech4humans/conditional-detr-50-signature-detector"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForObjectDetection.from_pretrained(model_name)
# Load and process image
image = Image.open("path/to/your/document.jpg")
inputs = processor(images=image, return_tensors="pt")
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Post-process results
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(
outputs, target_sizes=target_sizes, threshold=0.5
)[0]
# Extract detections
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(f"Detected signature with confidence {round(score.item(), 3)} at location {box}")
```
### **Visualization**
```python
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
def visualize_predictions(image_path, results, threshold=0.5):
image = Image.open(image_path)
fig, ax = plt.subplots(1, figsize=(12, 9))
ax.imshow(image)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
if score > threshold:
x, y, x2, y2 = box.tolist()
width, height = x2 - x, y2 - y
rect = patches.Rectangle(
(x, y), width, height,
linewidth=2, edgecolor='red', facecolor='none'
)
ax.add_patch(rect)
ax.text(x, y-10, f'Signature: {score:.3f}',
bbox=dict(boxstyle="round,pad=0.3", facecolor="yellow", alpha=0.7))
ax.set_title("Signature Detection Results")
plt.axis('off')
plt.show()
# Use the visualization
visualize_predictions("path/to/your/document.jpg", results)
```
---
## **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, Code and Training Materials – **Apache 2.0**
- **License:** Apache License 2.0
- **Usage:** All training scripts, deployment code, and usage instructions are licensed under the Apache 2.0 license.
---
## **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> |