template.yaml

name: instance-segmentation-0002
domain: Instance Segmentation
problem: COCO Instance Segmentation
framework: OTEDetection v2.9.1
summary: Instance segmentation based on Mask R-CNN architecture with ResNet50.
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: 42aa3d0009d2ed454406c1270bba9da70f6e5c7a6a96fa447fd568302bf700d1
  size: 177867103
  source: https://download.01.org/opencv/openvino_training_extensions/models/instance_segmentation/v2/instance-segmentation-0002.pth
  destination: snapshot.pth
- source: ../../../../../ote/tools/train.py
  destination: train.py
- source: ../../../../../ote/tools/eval.py
  destination: eval.py
- source: ../../../../../ote/tools/export.py
  destination: export.py
- source: ../../../../../ote/tools/compress.py
  destination: compress.py
- source: ../../../../../ote
  destination: packages/ote
- source: ../../requirements.txt
  destination: requirements.txt
dataset_requirements:
  classes: [person, bicycle, car, motorcycle, airplane, bus, train, truck, boat,
    traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse,
    sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie,
    suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove,
    skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork,
    knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog,
    pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv,
    laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator,
    book, clock, vase, scissors, teddy bear, hair drier, toothbrush]
max_nodes: 1
training_target:
- GPU
inference_target:
- CPU
hyper_parameters:
  basic:
    batch_size: 16
    base_learning_rate: 0.02
    epochs: 36
output_format:
  onnx:
    default: true
  openvino:
    default: true
    input_format: BGR
optimisations:
  nncf_quantization:
    config: compression_config.json
    default: false
metrics:
- display_name: Bbox AP @ [IoU=0.50:0.95]
  key: ap
  unit: '%'
  value: 40.8
- display_name: Segm AP @ [IoU=0.50:0.95]
  key: ap
  unit: '%'
  value: 36.9
- display_name: Size
  key: size
  unit: Mp
  value: 47.58
- display_name: Complexity
  key: complexity
  unit: GFLOPs
  value: 423.02
gpu_num: 8
config: model.py
tensorboard: true
estimated_batch_time: -1