| | ---
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| | language: en
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| | license: apache-2.0
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| | model_name: tinyyolov2-7.onnx
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| | tags:
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| | - validated
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| | - vision
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| | - object_detection_segmentation
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| | - tiny-yolov2
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| | ---
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| | <!--- SPDX-License-Identifier: MIT -->
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| |
|
| | # Tiny YOLOv2
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| |
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| | ## Description
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| | This model is a real-time neural network for object detection that detects 20 different classes. It is made up of 9 convolutional layers and 6 max-pooling layers and is a smaller version of the more complex full [YOLOv2](https://pjreddie.com/darknet/yolov2/) network.
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| |
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| | CoreML TinyYoloV2 ==> ONNX TinyYoloV2
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| |
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| | ## Model
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| | |Model|Download|Download (with sample test data)| ONNX version |Opset version|
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| | |-----|:-------|:-------------------------------|:-------------|:------------|
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| | |Tiny YOLOv2|[62 MB](model/tinyyolov2-7.onnx)|[59 MB](model/tinyyolov2-7.tar.gz) |1.2 |7 |
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| | |Tiny YOLOv2|[62 MB](model/tinyyolov2-8.onnx)|[59 MB](model/tinyyolov2-8.tar.gz) |1.3 |8 |
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| |
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| | ### Paper
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| | "YOLO9000: Better, Faster, Stronger" [arXiv:1612.08242](https://arxiv.org/pdf/1612.08242.pdf)
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| |
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| | ### Dataset
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| | The Tiny YOLO model was trained on the [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) dataset.
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| |
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| | ### Source
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| | The model was converted from a Core ML version of Tiny YOLO using [ONNXMLTools](https://github.com/onnx/onnxmltools). The source code can be found [here](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). The Core ML model in turn was converted from the [original network](https://pjreddie.com/darknet/yolov2/) implemented in Darknet (via intermediate conversion through Keras).
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| |
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| | ## Inference
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| | ### Input
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| | shape `(1x3x416x416)`
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| | ### Preprocessing
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| | ### Output
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| | shape `(1x125x13x13)`
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| | ### Postprocessing
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| | The output is a `(125x13x13)` tensor where 13x13 is the number of grid cells that the image gets divided into. Each grid cell corresponds to 125 channels, made up of the 5 bounding boxes predicted by the grid cell and the 25 data elements that describe each bounding box (`5x25=125`). For more information on how to derive the final bounding boxes and their corresponding confidence scores, refer to this [post](http://machinethink.net/blog/object-detection-with-yolo/).
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| | ### Sample test data
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| | Sets of sample input and output files are provided in
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| | * serialized protobuf TensorProtos (`.pb`), which are stored in the folders `test_data_set_*/`.
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| |
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| | ## License
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| | MIT
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| |
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| |
|
