template.yaml

name: vehicle-detection-0200
domain: Object Detection
problem: Vehicle Detection
framework: OTEDetection v2.9.1
summary: Vehicle Detection based on MobileNetV2 (SSD).
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: d66bbe25f8d8811a88a2c405310ea4cda968f4f2f7991a5bddcfbe7d40293984
  size: 14905214
  source: https://download.01.org/opencv/openvino_training_extensions/models/object_detection/v2/vehicle-detection-0200-1.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
max_nodes: 1
training_target:
- CPU
- GPU
inference_target:
- CPU
- iGPU
- VPU
hyper_parameters:
  basic:
    batch_size: 185
    base_learning_rate: 0.05
    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: 0.82
- display_name: AP @ [IoU=0.50:0.95]
  key: ap
  unit: '%'
  value: 25.4
gpu_num: 4
tensorboard: true
config: model.py