definition.yaml

benchmark:
  MS COCO Validation:
    mAP: 0.331
description: Yolo v3 Tiny is a object detection network, that localizes and identifies
  objects in an input image. This is a floating point version that takes a 416x416
  input image and outputs detections for this image. This model is generated using
  the weights from the [https://pjreddie.com/darknet/yolo/](YOLO website).
license:
- Apache-2.0
network:
  file_size_bytes: 35455980
  filename: yolo_v3_tiny_darknet_fp32.tflite
  framework: TensorFlow Lite
  hash:
    algorithm: sha1
    value: b38f7be6856eed4466493bdc86be1879f4b743fb
  provenance: https://pjreddie.com/media/files/yolov3-tiny.weights & https://github.com/mystic123/tensorflow-yolo-v3
network_parameters:
  input_nodes:
  - description: A 416x416 floating point input image.
    example_input:
      path: models/object_detection/yolo_v3_tiny/tflite_fp32/testing_input/inputs
    name: inputs
    shape:
    - 1
    - 416
    - 416
    - 3
  output_nodes:
  - description: A 1xNx85 map of predictions, where the first 4 entries of the 3rd
      dimension are the bounding box coordinates and the 5th is the confidence. The
      remaining entries are softmax scores for each class.
    name: output_boxes
    shape:
    - 1
    - 2535
    - 85
    test_output_path: models/object_detection/yolo_v3_tiny/tflite_fp32/testing_output/output_boxes
operators:
  TensorFlow Lite:
  - ADD
  - CONCATENATION
  - CONV_2D
  - EXP
  - LOGISTIC
  - MAXIMUM
  - MAX_POOL_2D
  - MUL
  - RESHAPE
  - RESIZE_NEAREST_NEIGHBOR
  - SPLIT_V
  - SUB
paper: https://arxiv.org/abs/1804.02767