Drawing Detection Yolov8

--- license: agpl-3.0 language: - en - ru pipeline_tag: object-detection library_name: ultralytics tags: - computer-vision - object-detection - yolov8 - ultralytics - drawing-detection base_model: ultralytics/yolov8n --- # YOLOv8n Drawing Detection Model This model is a fine-tuned version of the ultralytics YOLOv8 Nano (`yolov8n`) model for detecting elements and objects within drawings/sketches. ## Model Details - **Model type:** Object Detection (YOLOv8 architecture) - **Base Model:** `ultralytics/yolov8n` (Nano version for real-time performance) - **Library:** Ultralytics - **License:** AGPL-3.0 ## Intended Use This model is intended for computer vision applications where identifying bounding boxes and specific classes of objects within hand-drawn sketches or digital drawings is required. ## Training Parameters The model was trained using the Ultralytics framework with the following hyper-parameters: - **Epochs:** 100 - **Image Size:** 640x640 - **Batch Size:** 8 - **Workers:** 4 - **Optimizer:** Auto-selected by YOLOv8 - **Early Stopping Patience:** 50 epochs ## Hardware Environment - **PyTorch Version:** 2.7.1+cu118 - **Hardware:** NVIDIA RTX A4000 (15.6 GB VRAM) ## How to use You can load this model and run inference using the `ultralytics` package: ```python from ultralytics import YOLO # Load the model model = YOLO("hf_hub_username/drawing-detection-yolov8") # Perform object detection on an image results = model("path/to/your/drawing.jpg") # Show results results[0].show()

Source context: 3 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo andreysnosov89-ship-it/drawing-detection-yolov8