YOLOv8 Door Detection for Visually Impaired Individuals

Imagine a world where doors become more than just obstacles for the visually impaired. Our YOLOv8 project revolutionizes accessibility by using cutting-edge computer vision technology to detect doors.

Dataset

The dataset has been collected from New York and annotated through a combination of auto-annotation and manual enhancement.

Dataset Download: Download Dataset

Installation and Usage

1. Install the required library:

   pip install ultralytics

2. Load the model:

   from ultralytics import YOLO
   model = YOLO("yolov8s.pt")  # Load the pretrained model

3. Training the model:

   model.train(data='/content/data.yaml', epochs=47, imgsz=1280, batch=8)

4. Load the best model:

   model = YOLO("/content/runs/detect/train2/weights/best.pt")  # Load the best model

5. Making predictions:

   res = model.predict("/content/test.png", save=True, conf=0.3)

6. Visualizing results:

   import matplotlib.pyplot as plt
   import matplotlib.image as mpimg
   
   # Load your images
   image1 = mpimg.imread('/content/test.png')
   image2 = mpimg.imread('/content/runs/detect/predict2/test.png')
   
   # Plotting the images side by side
   plt.figure(figsize=(20, 20))
   plt.subplot(1, 2, 1)
   plt.imshow(image1)
   plt.title('Source Image')
   
   plt.subplot(1, 2, 2)
   plt.imshow(image2)
   plt.title('Predicted Image')
   
   plt.tight_layout()
   plt.show()

Here's a checklist of key points for YOLOv8 door detection project:

• Data Annotation:

  • Auto-annotate dataset using a cutting-edge solution.
  • Enhance annotations manually for improved accuracy.

• Model Training:

  • Load the pre-trained YOLOv8 model.
  • Train the model using the annotated dataset.
  • Evaluate model performance .

• Share Weights:

  • Save model weights after training.
  • Share trained model weights with the community.

• Share Data:

  • Provide access to the annotated dataset for transparency.

• Hugging Face deployment:

  • convert model into onnx
  • Deploy the trained YOLOv8 model on Hugging Face.

• API deployment:

  • convert model into onnx
  • FastAPI code for deployment.
  • docker image
  • deploymernt into AWS

• Mobile deployment:

  • convert model into tflite
  • flutter code.