template.yaml

name: instance-segmentation-0228
domain: Instance Segmentation
problem: COCO Instance Segmentation
framework: OTEDetection v2.1.1
summary: Instance segmentation based on Mask R-CNN architecture with ResNet101.
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: 044a09477e1a1c24607833211254f14dca23f3e856ed1d1f6b6529e15fdb64bf
  size: 383874216
  source: https://download.01.org/opencv/openvino_training_extensions/models/instance_segmentation/v2/instance-segmentation-0228.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: 39.0
- display_name: Segm AP @ [IoU=0.50:0.95]
  key: ap
  unit: '%'
  value: 33.9
- display_name: Size
  key: size
  unit: Mp
  value: 49.4374
- display_name: Complexity
  key: complexity
  unit: GFLOPs
  value: 147.19
gpu_num: 2
config: model.py
tensorboard: true
estimated_batch_time: -1