Hugging Face's logo

siavava
/
segformer-waterline-detection

Transformers
PyTorch
TensorBoard
segformer
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
segformer-waterline-detection / README.md
siavava's picture
siavava
update model card README.md
d47842c verified
preview code
|
raw
history blame contribute delete
3.29 kB
metadata
license: other
tags:
  - generated_from_trainer
datasets:
  - scene_parse_150
model-index:
  - name: segformer-b0-scene-parse-150
    results: []

segformer-b0-scene-parse-150

This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • eval_loss: 4.9114
  • eval_mean_iou: 0.0130
  • eval_mean_accuracy: 0.0567
  • eval_overall_accuracy: 0.2065
  • eval_per_category_iou: [0.006025927531255453, 0.23336811824661952, 0.5164444271242657, 0.09256597061475111, 0.13041514146963668, 0.03079454026681747, 0.3643351171640548, 0.0, 0.07230009838464191, 0.018990561238908042, 0.0, 0.0, 0.00021751543000081568, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05118970203258133, 0.0843910203406648, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.006517548422630887, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0]
  • eval_per_category_accuracy: [0.006075413445931374, 0.24739890483284213, 0.6689475307776438, 0.10182529684526521, 0.31975958171127, 0.033484264072893954, 0.4822156415844549, 0.0, 0.1105070368228263, 0.02761318529597883, 0.0, 0.0, 0.0002495788357147314, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.3219604278822625, 0.23767246899924319, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.05148658448150834, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan]
  • eval_runtime: 16.6035
  • eval_samples_per_second: 0.602
  • eval_steps_per_second: 0.301
  • epoch: 1.0
  • step: 20

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.0
  • Datasets 2.12.0
  • Tokenizers 0.13.3