object_detection.rst

Object Detection Models

This subpackage provides a pre-trained state-of-the-art models for the purpose of object detection which is trained on ImageNet dataset and fine-tuned on Pascal VOC and MS COCO dataset.

The pre-trained models can be used for both inference and training as following:

# Import required modules
import nnabla as nn
from nnabla.models.object_detection import YoloV2
from nnabla.models.object_detection.utils import (
    LetterBoxTransform,
    draw_bounding_boxes)
from nnabla.utils.image_utils import imread, imsave
import numpy as np

# Set device
from nnabla.ext_utils import get_extension_context
nn.set_default_context(get_extension_context('cudnn', device_id='0'))

# Load and create a detection model
h, w = 608, 608
yolov2 = YoloV2('coco')
x = nn.Variable((1, 3, h, w))
y = yolov2(x)

# Load an image and scale it to fit inside the (h, w) frame
img_orig = imread('dog.jpg')
lbt = LetterBoxTransform(img_orig, h, w)

# Execute detection
x.d = lbt.image.transpose(2, 0, 1)[None]
y.forward(clear_buffer=True)

# Draw bounding boxes to the original image
bboxes = lbt.inverse_coordinate_transform(y.d[0])
img_draw = draw_bounding_boxes(
    img_orig, bboxes, yolov2.get_category_names())
imsave("detected.jpg", img_draw)

Available models trained on COCO dataset

Name	Class	mAP	Training framework	Notes
YOLO v2	YoloV2	44.12	Darknet	Weights converted from author's model

Available models trained on VOC dataset

Name	Class	mAP	Training framework	Notes
YOLO v2	YoloV2	76.00	Darknet	Weights converted from author's model

Common interfaces

nnabla.models.object_detection.base

ObjectDetection

nnabla.models.object_detection.utils

LetterBoxTransform

draw_bounding_boxes

List of models

nnabla.models.object_detection

YoloV2