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metadata
base_model: ''
tags:
  - generated_from_trainer
model-index:
  - name: glacformer
    results: []

glacformer

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0333
  • Mean Iou: 0.9528
  • Mean Accuracy: 0.9772
  • Overall Accuracy: 0.9885
  • Per Category Iou: [0.9855230058020051, 0.8845759711828091, 0.9883964861024538]
  • Per Category Accuracy: [0.9921669407092866, 0.9462930795421282, 0.9931901963885149]

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: 4
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
0.045 1.0 523 0.0421 0.9477 0.9795 0.9869 [0.9852157890390353, 0.8719898483736556, 0.9857700613925825] [0.9904340924248899, 0.9587586082053337, 0.9893900149083925]
0.0372 2.0 1046 0.0333 0.9528 0.9772 0.9885 [0.9855230058020051, 0.8845759711828091, 0.9883964861024538] [0.9921669407092866, 0.9462930795421282, 0.9931901963885149]

Framework versions

  • Transformers 4.31.0
  • Pytorch 1.14.0.dev20221130+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3