-
Notifications
You must be signed in to change notification settings - Fork 435
/
template.yaml
67 lines (67 loc) · 1.66 KB
/
template.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
name: efficientnet_b2b-mask_rcnn-576x576
domain: Instance Segmentation
problem: Custom Instance Segmentation
framework: OTEDetection v2.9.1
summary: Custom instance segmentation based on Mask R-CNN architecture with EfficientNet-b2.
annotation_format: COCO
initial_weights: snapshot.pth
dependencies:
- sha256: b00407a212bdba77866b6c337a24e997b4b2621afb50e62b261df1a4f8efd3c6
size: 53452621
source: https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/models/instance_segmentation/v2/efficientnet_b2b-mask_rcnn-576x576.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
max_nodes: 1
training_target:
- GPU
inference_target:
- CPU
hyper_parameters:
basic:
batch_size: 8
base_learning_rate: 0.025
epochs: 12
extra:
custom_classes:
name: Custom classes
param: --classes
type: string
default: ''
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: 35.2
- display_name: Segm AP @ [IoU=0.50:0.95]
key: ap
unit: '%'
value: 31.0
- display_name: Size
key: size
unit: Mp
value: 13.27
- display_name: Complexity
key: complexity
unit: GFLOPs
value: 26.92
gpu_num: 1
config: model.py
tensorboard: true