template.yaml

name: person-detection-0203
domain: Object Detection
problem: Person Detection
framework: OTEDetection v2.9.1
summary: Person Detection based on MobileNetV2 (SSD).
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: 2e2020ee31fc8eb7028b633b06704e7488b8dbc9f6e207557639bec13e7a4746
  size: 15854322
  source: https://download.01.org/opencv/openvino_training_extensions/models/object_detection/v2/person-detection-0203.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
max_nodes: 1
training_target:
- GPU
inference_target:
- CPU
- iGPU
hyper_parameters:
  basic:
    batch_size: 14
    base_learning_rate: 0.025
    epochs: 20
output_format:
  onnx:
    default: true
  openvino:
    default: true
    input_format: BGR
optimisations:
  nncf_quantization:
    config: compression_config.json
    default: false
metrics:
- display_name: Size
  key: size
  unit: Mp
  value: 1.83
- display_name: Complexity
  key: complexity
  unit: GFLOPs
  value: 6.74
- display_name: AP @ [IoU=0.50:0.95]
  key: ap
  unit: '%'
  value: 40.8
gpu_num: 2
tensorboard: true
config: model.py