| --- |
| license: apache-2.0 |
| language: |
| - en |
| - it |
| - de |
| - es |
| base_model: |
| - Ultralytics/YOLOv8 |
| pipeline_tag: object-detection |
| library_name: ultralytics |
| library_version: 8.3.66 |
| inference: false |
| tags: |
| - ultralytics |
| - yolov8 |
| - yolo |
| - vision |
| - object-detection |
| - pytorch |
| --- |
| ## MaterialSpecVision 🔎 |
|
|
| **release 1.0** |
|
|
|  |
|
|
| --- |
|
|
| ### WHAT IS MaterialSpecVision? |
|
|
| This is a very specific object detection model designed to **detect heat numbers in material |
| certificates**, solving the challenge of locating them manually in poorly scanned or low-quality |
| PDF documents. |
|
|
| ### WHY USE MaterialSpecVision? |
|
|
| Heat numbers, which are critical for traceability in material documentation, can often be |
| difficult to identify due to inconsistent formatting, low resolution, or cluttered layouts. |
|
|
| ### WHO SHOULD USE MaterialSpecVision |
|
|
| Ideal for engineers and quality control teams, it streamlines the task of parsing certificates, |
| even under challenging conditions. |
|
|
| ### TECHNICAL DETAILS |
|
|
| **Trained on a dataset of over 2,000 material certificates** in German, Italian, Spanish, and Chinese, |
| ensuring robust performance across a wide range of formats and languages. Model ensures high |
| accuracy and reliability, effectively identifying and highlighting heat numbers even in low quality |
| material certificates. |
|
|
| ```python |
| """Model Training Configuration:""" |
| model = YOLO('yolov8n.pt') |
| model.train(epochs=100, batch=16, imgsz=640) |
| ``` |
|  |
|
|
| ### USAGE EXAMPLES |
|
|
| * Single-page Material Certificate (JPG format) |
| ```python |
| import matplotlib.pyplot as plt |
| from ultralytics import YOLO |
| |
| model = YOLO("best.pt") # the current pre-trained model file you download from HF |
| results = model("your_material_certificate_file.jpg") |
| |
| for r in results: |
| filtered_boxes = r.boxes[r.boxes.conf > 0.4] # filtering confidence |
| r.boxes = filtered_boxes |
| im_bgr = r.plot(line_width=5, font_size=5, conf=True) |
| plt.imshow(im_bgr) |
| plt.axis('off') |
| plt.show() |
| ``` |
| * Multi-page Material Certificate (PDF format)</br> |
| For PDFs, you need to convert each page to JPG images before processing. This requires installing |
| **Tesseract** and **Poppler**. |
| ```python |
| import pytesseract |
| from pdf2image import convert_from_path |
| import os |
| |
| import matplotlib.pyplot as plt |
| from ultralytics import YOLO |
| |
| custom_config = r'--oem 3 --psm 4' |
| pytesseract.pytesseract.tesseract_cmd = r'\...\Tesseract-OCR\tesseract.exe' # download and install Tesseract |
| poppler_path=r'\...\poppler-24.08.0\Library\bin' # download and install poppler |
| |
| pdf_file_name = "your_material_certificate.pdf" |
| pdf_file_path = rf":\...\{pdf_file_name}" # absolute path to your material certificate |
| images_to_process = [] |
| model = YOLO("best.pt") |
| |
| try: |
| images = convert_from_path(pdf_file_path, |
| poppler_path=poppler_path, |
| dpi=300, |
| use_cropbox=True) |
| for i, image in enumerate(images): |
| orientation_data = pytesseract.image_to_osd(image) |
| rotation_angle = int(orientation_data.split("Rotate: ")[1].split("\n")[0]) |
| full_path_img = f"{os.path.join(os.getcwd(), str(i) + pdf_file_name[:-4])}.jpg" |
| images_to_process.append(full_path_img) |
| if rotation_angle != 0: |
| rotated_image = image.rotate(-rotation_angle, expand=True) |
| rotated_image.save(full_path_img, "JPEG") |
| else: |
| image.save(full_path_img, "JPEG") |
| except: |
| print(f"PDF with file name {pdf_file_name} didn't processed ") |
| |
| for img_path in images_to_process: |
| results = model(img_path) |
| for r in results: |
| filtered_boxes = r.boxes[r.boxes.conf > 0.1] # filtered confidence |
| r.boxes = filtered_boxes |
| im_bgr = r.plot(line_width=5, font_size=5, conf=True) |
| plt.imshow(im_bgr) |
| plt.axis('off') |
| plt.show() |
| ``` |
|
|
| ### IF DETECTION ISN'T WORKING AS EXPECTED. |
| If detection doesn't work as expected, I’d love to review your material certificates for |
| further investigation and potential inclusion in the next training session. Feel free to reach me out via my linkedin account: http://linkedin.com/in/sergey-zhmaev-7a325896 |
|
|
| ### CITATION |
| If you use this model, please cite it as follows: |
|
|
| ```yaml |
| cff-version: 1.2.0 |
| message: "If you use this model, please cite:" |
| authors: |
| - family-names: Zhmaev |
| given-names: Sergey |
| title: "Material Specifications AI Vision Model" |
| version: 1.0 |
| doi: 10.57967/hf/4255 |
| ``` |
|
|
| ### LICENSE |
|
|
| **Apache 2.0** |
