template.yaml

name: person-vehicle-bike-detection-2003
domain: Object Detection
problem: Person Vehicle Bike Detection
framework: OTEDetection v2.9.1
summary: Person Vehicle Bike Detection based on MobileNetV2 (SSD).
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: d2d53b376229c002d7204c1f8ad10fbae5f065fbabb7e4ef7a67a1905168f2f6
  size: 15863668
  source: https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/models/object_detection/v2/vehicle-person-bike-detection-2003.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:
  - vehicle
  - person
  - non-vehicle
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: ~
metrics:
- display_name: Size
  key: size
  unit: Mp
  value: 1.95
- display_name: Complexity
  key: complexity
  unit: GFLOPs
  value: 6.78
- display_name: mAP @ [IoU=0.50:0.95]
  key: map
  unit: '%'
  value: 33.6
gpu_num: 2
tensorboard: true
config: model.py