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metadata
license: apache-2.0
base_model: microsoft/resnet-18
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Action_agent_small_34_class
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.09523809523809523

Action_agent_small_34_class

This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: nan
  • Accuracy: 0.0952

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.0 0.32 100 nan 0.0952
0.0 0.64 200 nan 0.0952
0.0 0.96 300 nan 0.0952
0.0 1.27 400 nan 0.0952
0.0 1.59 500 nan 0.0952
0.0 1.91 600 nan 0.0952
0.0 2.23 700 nan 0.0952
0.0 2.55 800 nan 0.0952
0.0 2.87 900 nan 0.0952
0.0 3.18 1000 nan 0.0952
0.0 3.5 1100 nan 0.0952
0.0 3.82 1200 nan 0.0952
0.0 4.14 1300 nan 0.0952
0.0 4.46 1400 nan 0.0952
0.0 4.78 1500 nan 0.0952
0.0 5.1 1600 nan 0.0952
0.0 5.41 1700 nan 0.0952
0.0 5.73 1800 nan 0.0952
0.0 6.05 1900 nan 0.0952
0.0 6.37 2000 nan 0.0952
0.0 6.69 2100 nan 0.0952
0.0 7.01 2200 nan 0.0952
0.0 7.32 2300 nan 0.0952
0.0 7.64 2400 nan 0.0952
0.0 7.96 2500 nan 0.0952
0.0 8.28 2600 nan 0.0952
0.0 8.6 2700 nan 0.0952
0.0 8.92 2800 nan 0.0952
0.0 9.24 2900 nan 0.0952
0.0 9.55 3000 nan 0.0952
0.0 9.87 3100 nan 0.0952

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2