onnxmodelzoo/tiny-yolov3-11

language: en license: apache-2.0 model_name: tiny-yolov3-11.onnx tags:

• validated
• vision
• object_detection_segmentation
• tiny-yolov3

Tiny YOLOv3

Description

This model is a neural network for real-time object detection that detects 80 different classes. It is very fast and accurate. It is a smaller version of YOLOv3 model.

Model

Inference

Input to model

Resized image (1x3x416x416) Original image size (1x2) which is [image.size[1], image.size[0]]

Preprocessing steps

The images have to be loaded in to a range of [0, 1]. The transformation should preferrably happen at preprocessing.

The following code shows how to preprocess a NCHW tensor:

import numpy as np
from PIL import Image

# this function is from yolo3.utils.letterbox_image
def letterbox_image(image, size):
'''resize image with unchanged aspect ratio using padding'''
iw, ih = image.size
w, h = size
scale = min(w/iw, h/ih)
nw = int(iw*scale)
nh = int(ih*scale)

image = image.resize((nw,nh), Image.BICUBIC)
new_image = Image.new('RGB', size, (128,128,128))
new_image.paste(image, ((w-nw)//2, (h-nh)//2))
return new_image

def preprocess(img):
model_image_size = (416, 416)
boxed_image = letterbox_image(img, tuple(reversed(model_image_size)))
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.transpose(image_data, [2, 0, 1])
image_data = np.expand_dims(image_data, 0)
return image_data

image = Image.open(img_path)
# input
image_data = preprocess(image)
image_size = np.array([image.size[1], image.size[0]], dtype=np.float32).reshape(1, 2)

Output of model

The model has 3 outputs. boxes: (1x'n_candidates'x4), the coordinates of all anchor boxes, scores: (1x80x'n_candidates'), the scores of all anchor boxes per class, indices: ('nbox'x3), selected indices from the boxes tensor. The selected index format is (batch_index, class_index, box_index). The class list is here

Postprocessing steps

Post processing and meaning of output

out_boxes, out_scores, out_classes = [], [], []
for idx_ in indices[0]:
out_classes.append(idx_[1])
out_scores.append(scores[tuple(idx_)])
idx_1 = (idx_[0], idx_[2])
out_boxes.append(boxes[idx_1])

out_boxes, out_scores, out_classes are list of resulting boxes, scores, and classes.

Dataset (Train and validation)

We use pretrained weights from pjreddie.com here.

Validation accuracy

Metric is COCO box mAP (averaged over IoU of 0.5:0.95), computed over 2017 COCO val data. mAP of 0.331 based on original tiny Yolov3 model here

Publication/Attribution

Joseph Redmon, Ali Farhadi. YOLOv3: An Incremental Improvement, paper

References

This model is converted from a keras model repository using keras2onnx converter repository.

License

MIT License