Image Object Recognition based on Deep Neural Network using Keras
Budget: $170 – $240 USD
The Goal:
Detect and Count number of teeth on the excavator bucket - see image attached.
The Task:
1. Take video dataset (6 videos) provided by me and label it using feely available tools (LabelMe, LabelImg).
2. Build a deep neural network (semantic segmentation or object detection) to recognize excavator bucket teeth.
3. Train the network.
4. Test and show detection results as on the image attached.
Deliverables:
1. Conda/Python environment specification.
2. Python script that loads labeled data, defines and trains the network
3. Labeled dataset and instructions how to label
4. Python scripts that runs and detects teeth on the new video file.
Detect and Count number of teeth on the excavator bucket - see image attached.
The Task:
1. Take video dataset (6 videos) provided by me and label it using feely available tools (LabelMe, LabelImg).
2. Build a deep neural network (semantic segmentation or object detection) to recognize excavator bucket teeth.
3. Train the network.
4. Test and show detection results as on the image attached.
Deliverables:
1. Conda/Python environment specification.
2. Python script that loads labeled data, defines and trains the network
3. Labeled dataset and instructions how to label
4. Python scripts that runs and detects teeth on the new video file.
Related categories:
Python
Windows Desktop
Machine Learning (ML)
Computer Vision
Machine Vision / Video Analytics