EfficientNetV2 | moritzm00 Overview – Weights & Biases

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Moritzm00's group workspace

Group: EfficientNetV2

1-19

of 19

Tags

Notes

Tags

EfficientNetV2B0

Author

State

Finished

Start time

May 13th, 2023 12:38:16 PM

Runtime

15m 1s

Tracked hours

14m 57s

Run path

moritzm00/UTKFace-Age-Regression/032o75mo

OS

Linux-5.4.0-122-generic-x86_64-with-glibc2.31

Python version

3.9.16

Git repository

git clone https://github.com/MoritzM00/AgePrediction-UTKFace.git

Git state

git checkout -b "cerulean-wind-117" 09b3e1a06a101211ee9034f980a88d2c5b376e8e

Command

/usr/local/bin/train model=efficientnetv2 model.version=B0 train.batch_size=64 train.target_size=[150,150] model.finetune_base=true

System Hardware

CPU count	8
Logical CPU count	8
GPU count	1
GPU type	NVIDIA RTX A4000

W&B CLI Version

0.15.2

Group

Config parameters are your model's inputs. Learn more

▶
Config parameters:{} 12 keys
- ▶
  augment:{} 2 keys
  - active:
    true
  - ▶
    factors:{} 4 keys
    - random_brightness:
      null
    - random_flip:
      "horizontal"
    - random_rotation:
      0.1
    - random_translation:
      null
- ▶
  callbacks:{} 7 keys
- ckpt_dir:
  "ckpts"
- data_dir:
  "data"
- ▶
  dataset:{} 6 keys
- log_dir:
  "logs"
- ▶
  lr_schedule:{} 2 keys
  - ▶
    stage_1:{} 4 keys
    - _target_:
      "tensorflow.keras.optimizers.schedules.ExponentialDecay"
    - decay_rate:
      0.96
    - decay_steps:
      100,000
    - initial_learning_rate:
      0.004
  - ▶
    stage_2:{} 4 keys
    - _target_:
      "tensorflow.keras.optimizers.schedules.ExponentialDecay"
    - decay_rate:
      0.99
    - decay_steps:
      100,000
    - initial_learning_rate:
      0.00005
- ▶
  model:{} 9 keys
- model_dir:
  "models"
- ▶
  optimizer:{} 2 keys
  - _partial_:
    true
  - _target_:
    "tensorflow.keras.optimizers.Adam"
- ▶
  train:{} 10 keys
- ▶
  wandb:{} 9 keys

Summary metrics are your model's outputs. Learn more

▶
Summary metrics:{} 11 keys
- ▶
  age_distribution:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/age_distribution_29_7e287abbdcbea92c0494.plotly.json"
  - sha256:
    "7e287abbdcbea92c04942bf9051864118a50c98569dc0606f718c1513c191fda"
  - size:
    147,110
- ▶
  empirical_cdfs:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/empirical_cdfs_29_3bb5f1d753fd24392cc6.plotly.json"
  - sha256:
    "3bb5f1d753fd24392cc6f5bb0d0f2f4289ea87d6a8ff4b6fd625daae01e6b32c"
  - size:
    302,650
- epoch/epoch:
  28
- epoch/learning_rate:
  0.0000499716006743256
- epoch/loss:
  58.790645599365234
- epoch/mae:
  5.781992435455322
- epoch/val_loss:
  59.09527969360352
- epoch/val_mae:
  5.570091724395752
- ▶
  predictions:{} 7 keys
- ▶
  residuals:{} 4 keys
  - _type:
    "plotly-file"
  - path:
    "media/plotly/residuals_29_6450115ecaf5a9161bee.plotly.json"
  - sha256:
    "6450115ecaf5a9161beed0414e1299fdcce991825a6adb3c281a299cd94ff851"
  - size:
    113,046
- test_mae:
  5.545294129662452

This run produced these artifacts as outputs. Total: 2. Learn more

run_032o75mo_model:v0

run-032o75mo-history:v0

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