How do I train my SSD model?

How do I train my SSD model?

We recommend using examples/ssd….

1. Following the original instructions to compile SSD. Make sure that you can run it successfully.
2. Step 2: Prepare your new dataset. For convenience, please follow the VOC dataset format to make the new dataset.
3. Step 3: Generate LMDB file.
4. Step 4: Train SSD on the new dataset.
5. Step 5: Run.

How would you train your own object detection with TensorFlow object detection API?

Installation and setup

1. Creating a project directory. Under a path of your choice, create a new folder.
2. Creating a new virtual environment.
3. Download and extract TensorFlow Model Garden.
4. Download, install and compile Protobuf.
5. Install COCO API.
6. Object Detection API installation.

What is ssd object detection?

SSD is a single-shot detector. It has no delegated region proposal network and predicts the boundary boxes and the classes directly from feature maps in one single pass. To improve accuracy, SSD introduces: small convolutional filters to predict object classes and offsets to default boundary boxes.

What is mobilenet-ssd?

The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework. For details about this model, check out the repository.

Is mobilenet an SSD?

The mobilenet-ssd model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. This model is implemented using the Caffe* framework.

Is SSD deep learning?

We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location.