template.yaml

name: vehicle-detection-0203
domain: Object Detection
problem: Vehicle Detection
framework: OTEDetection v2.9.1
summary: Vehicle Detection based on ResNet18+fpn+CascadeRCNN.
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: d77e6bcf1af3e94508f67d849a8ca8adf6e09c7360c8f6cfc97fa94e23621a22
  size: 193586663
  source: https://download.01.org/opencv/openvino_training_extensions/models/object_detection/v3/vehicle-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:
  - vehicle
max_nodes: 1
training_target:
- GPU
inference_target:
- CPU
- iGPU
- VPU
hyper_parameters:
  basic:
    batch_size: 4
    base_learning_rate: 0.02
    epochs: 16
output_format:
  onnx:
    default: true
  openvino:
    default: true
    input_format: BGR
optimisations: ~
metrics:
- display_name: Size
  key: size
  unit: Mp
  value: 24.11
- display_name: Complexity
  key: complexity
  unit: GFLOPs
  value: 112.34
- display_name: AP @ [IoU=0.50:0.95]
  key: ap
  unit: '%'
  value: 43.5
gpu_num: 1
tensorboard: true
config: model.py