This model detects popular brand logos within images. This model can be used to search, edit, or count the number of logos that occur in a set of images or video frames.
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36.7% Mean Average Precision
This model was trained on an 80% subset of the Logos in the Wild dataset. This large-scale dataset contains web collected images with annotations for 871 brands. This model was tested on 20% of the images and achieves a mean Average Precision (mAP) score of 0.367.
A higher precision score indicates that the majority of labels predicted by the model for different classes are accurate.
This model is based on the YOLOv3 deep neural network. YOLOv3 is a single-shot object detection architecture, only requiring a single forward pass of the input through the network in order to make detection predictions. In order to detect objects of different sizes, YOLOv3 detects objects at 3 different scales at different branches throughout its architecture. The prediction branch at each scale is responsible for predicting candidate bounding boxes and object classes. These candidate bounding boxes are then consolidated using a technique known as Non-Maximum Suppression to generate the final output.
This model was trained on a subset of the Logos in the Wild dataset, which consists of labeled bounding boxes and their corresponding brand names in 6,809 “in the wild” images. Each image contains varying numbers of logo classes and instances. Roughly 80% of these images were used for training. The model was trained for 55 epochs on an NVIDIA Tesla V100 GPU, using Adam optimization with an initial learning rate 0.001.
Validation was performed on roughly 20% of the dataset.
The input(s) to this model must adhere to the following specifications:
This model will output the following:
The output file (“results.json”) will contain detected logo bounding boxes. Each bounding box will contain the corresponding logo class name, confidence score, and top left/bottom right x,y coordinates defining the box. This model can detect the following logos:
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