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template.yaml
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template.yaml
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name: instance-segmentation-1040
domain: Instance Segmentation
problem: COCO Instance Segmentation
framework: OTEDetection v2.9.1
summary: Instance segmentation based on Mask R-CNN architecture with EfficientNet-B2.
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: d87986c1f5ca481fc22e967fac7e02ae2058d0f453031e7257d1cb52c54c0f75
size: 106609665
source: https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/models/instance_segmentation/v2/instance-segmentation-1040.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: 16
output_format:
onnx:
default: true
openvino:
default: true
input_format: BGR
optimisations: ~
metrics:
- display_name: Bbox AP @ [IoU=0.50:0.95]
key: ap
unit: '%'
value: 35.3
- display_name: Segm AP @ [IoU=0.50:0.95]
key: ap
unit: '%'
value: 31.3
- display_name: Size
key: size
unit: Mp
value: 13.5673
- display_name: Complexity
key: complexity
unit: GFLOPs
value: 29.334
gpu_num: 2
config: model.py
tensorboard: true
estimated_batch_time: -1