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+---
+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: mAP@0.5
+ - type: precision
+ value: 0.653321
+ name: mAP@0.5:0.95
+---
+
+# **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 mAP@0.5 (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
[](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
[](https://www.gradio.app/) [](https://plotly.com/python/) |
+
+---
+
+---
+
+## **Dataset**
+
+
+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.
+
+
+
+
+ 
+ |
+
+ 
+ |
+
+
+---
+
+## **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.
+
+---
+
+## **Citation**
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{lima2024conditional-detr-signature-detection,
+ title={Conditional-DETR for Handwritten Signature Detection},
+ author={Lima, Samuel and Tech4Humans Team},
+ year={2024},
+ publisher={Hugging Face},
+ url={https://huggingface.co/tech4humans/conditional-detr-50-signature-detector}
+}
+```
+
+---
+
+## **Contact and Information**
+
+For further information, questions, or contributions, contact us at **iag@tech4h.com.br**.
+
+
+
+## **Author**
+
+
+
+
+
+
+ 
+ Samuel Lima
+
+ AI Research Engineer
+
+
+ 
+
+
+ |
+
+ Responsibilities in this Project
+
+ - 🔬 Model development and training
+ - 📊 Dataset analysis and processing
+ - ⚙️ Architecture selection and performance evaluation
+ - 📝 Technical documentation and model card
+
+ |
+
+
+
+
+ 
+
+