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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: resnet-101-finetuned_resnet101-sgd-optimizer20-autotags
    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: 0.8847619047619047

resnet-101-finetuned_resnet101-sgd-optimizer20-autotags

This model is a fine-tuned version of microsoft/resnet-101 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3318
  • Accuracy: 0.8848

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: 0.1
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.1302 0.99 65 1.0040 0.6724
1.1708 1.99 130 1.4856 0.5495
1.141 2.99 195 1.1486 0.6352
1.0119 3.99 260 0.8829 0.7314
0.8091 4.99 325 0.8301 0.7419
0.7878 5.99 390 0.8121 0.7333
0.6827 6.99 455 0.6047 0.7990
0.5525 7.99 520 0.6028 0.8048
0.5787 8.99 585 0.5183 0.8352
0.4797 9.99 650 0.4737 0.8543
0.4224 10.99 715 0.4943 0.8305
0.4389 11.99 780 0.4162 0.8629
0.4142 12.99 845 0.4000 0.8629
0.3144 13.99 910 0.3833 0.8695
0.2915 14.99 975 0.3688 0.8733
0.3302 15.99 1040 0.3643 0.8810
0.2954 16.99 1105 0.3446 0.8867
0.2186 17.99 1170 0.3571 0.8905
0.1812 18.99 1235 0.3334 0.8886
0.1911 19.99 1300 0.3318 0.8848

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.11.0
  • Tokenizers 0.13.2