YOLOv8 Object Detection Model
This is a custom trained YOLOv8 nano model for detecting:
- Flooding
- Potholes
- Waste Management
The model was trained for 200 epochs on a custom dataset.
Model Details
- Architecture: YOLOv8n (nano)
- Input Size: 640x640
- Export Format: ONNX
- Training Dataset: Custom dataset for 'Flooding', 'Potholes', 'Waste Management'
- Metrics (best epoch 65 on validation set):
- mAP50-95: 0.393
- mAP50: 0.606
- Flooding mAP50: 0.797
- Potholes mAP50: 0.430
- Waste Management mAP50: 0.591
Usage (Example)
This model is provided in ONNX format and can be used for inference in various environments. Here's how you might load it with ONNX Runtime in Python:
import onnxruntime as ort
import numpy as np
from PIL import Image
# Load the ONNX model
session = ort.InferenceSession("Jin0908/muncipal-problem-detection/best.onnx") # Adjust path if downloaded locally
# Prepare input image (example: resize to 640x640, normalize)
image = Image.open("your_image.jpg").resize((640, 640))
input_array = np.array(image) / 255.0 # Normalize to [0, 1]
input_array = np.transpose(input_array, (2, 0, 1)) # (H, W, C) to (C, H, W)
input_array = np.expand_dims(input_array, axis=0).astype(np.float32) # Add batch dimension
# Run inference
outputs = session.run(None, {"images": input_array}) # Corrected: doubled curly braces for literal dict syntax
# Process outputs (this part is specific to YOLOv8 ONNX output and requires custom parsing)
# The output format is typically [batch_size, num_boxes, 4 + num_classes] or similar.
print("Model output shape:", outputs[0].shape)
# Further processing would involve non-max suppression (NMS) and decoding bounding boxes.