Spatial-Target-Norm | ayush-thakur Workspace – Weights & Biases

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Ayush-thakur's group workspace

Group: Spatial-Target-Norm

3

1-1

of 1

Tags

Notes

Train EfficientNetB0 by stacking all the target spectrograms spatially

Tags

effnetb0

full-channel

Author

State

Finished

Start time

June 9th, 2021 4:22:06 AM

Runtime

3m 48s

Tracked hours

3m 48s

Run path

ayush-thakur/kaggle-seti-exp/2yvadsu1

OS

Linux-4.19.0-16-cloud-amd64-x86_64-with-debian-10.9

Python version

3.7.10

Command

SETI/train-seti-experiments.ipynb

System Hardware

CPU count	8
GPU count	1
GPU type	Tesla V100-SXM2-16GB

W&B CLI Version

0.10.31

Group

Spatial-Target-Norm

Job Type

train

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 14 keys
- architecture:
  "CNN"
- batch_size:
  64
- epochs:
  100
- group:
  "Spatial-Target-Norm"
- img_height:
  224
- img_width:
  224
- infra:
  "GCP"
- learning_rate:
  0.001
- model_name:
  "EffNetB0"
- num_folds:
  5
- num_labels:
  1
- seed:
  42
- train_val_split:
  0.2
- wandb_kernel:
  true

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 9 keys
- auc:
  0.9926592111587524
- best_epoch:
  2
- best_val_loss:
  0.23527973890304563
- epoch:
  7
- ▶
  graph:{} 4 keys
  - _type:
    "graph-file"
  - path:
    "media/graph/graph_summary_c749891c.graph.json"
  - sha256:
    "c749891cf0cb020147fb6224563e1b6b971d6201572eaf1ad1b5610a48537057"
  - size:
    1,126
- loss:
  0.10708609968423843
- Val AUC-ROC:
  0.9138754606246948
- val_auc:
  0.914025068283081
- val_loss:
  0.2942134439945221

This run produced these artifacts as outputs. Total: 1. Learn more

run-2yvadsu1-history:v0

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