onnxmodelzoo/tiny-yolov3-11

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

Model	Download	Download (with sample test data)	ONNX version	Opset version	Accuracy
Tiny YOLOv3	34 MB	33 MB	1.6	11	mAP of 0.331

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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.

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Dataset (Train and validation)

We use pretrained weights from pjreddie.com here.

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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

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Publication/Attribution

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

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References

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

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License

MIT License

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