# yolof¶

## Use Case and High-Level Description¶

YOLOF is a simple, fast, and efficient object detector without FPN. Model based on “You Only Look One-level Feature” paper. It was implemented in PyTorch* framework. Model used DarkNet-53 with Cross Stage Partial blocks as backbone. For details see repository. This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes. Mapping of class IDs to label names provided in <omz_dir>/data/dataset_classes/coco_80cl.txt file.

Metric

Value

Type

Detection

GFLOPs

175.37942

MParams

48.228

Source framework

PyTorch*

## Accuracy¶

Accuracy metrics obtained on Common Objects in Context (COCO) validation dataset for converted model.

Metric

Value

mAP

60.69%

COCO mAP (0.5)

66.23%

COCO mAP (0.5:0.05:0.95)

43.63%

## Input¶

### Original model¶

Image, name - image_input, shape - 1, 3, 608, 608, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is BGR. Mean values - [103.53, 116.28, 123.675].

### Converted model¶

Image, name - image_input, shape - 1, 3, 608, 608, format is B, C, H, W, where:

• B - batch size

• C - channel

• H - height

• W - width

Channel order is BGR.

## Output¶

### Original model¶

The list of instances. The postprocessing is implemented inside the model and is performed while inference of model. So each instance it is a object with next fields:

• detection box

• label - predicted class ID

• score - onfidence for the predicted class

Detection box has format [x_min, y_min, x_max, y_max], where:

• (x_min, y_min) - coordinates of the top left bounding box corner

• (x_max, y_max) - coordinates of the bottom right bounding box corner

### Converted model¶

The array of detection summary info, name - boxes, shape - 1, 504, 38, 38. The anchor values are 16,16, 32,32, 64,64, 128,128, 256,256, 512,512.

For each case format is B, N\*84, Cx, Cy, where

• B - batch size

• Cx, Cy - cell index

• N - number of detection boxes for cell

Detection box has format [x, y, h, w, class_id_1, …, class_id_80], where:

• (x, y) - raw coordinates of box center, multiply by corresponding anchors to get relative to the cell coordinates

• h, w - raw height and width of box, apply exponential function and multiply by corresponding anchors to get absolute height and width values

• class_id_1,…, class_id_80 - probability distribution over the classes in logits format, apply sigmoid function to get confidence of each class

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

omz_downloader --name <model_name>
omz_converter --name <model_name>