Hugging Face's logo

hkivancoral
/
hushem_1x_deit_tiny_adamax_lr00001_fold5

Image Classification
Transformers
TensorBoard
Safetensors
vit
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
hushem_1x_deit_tiny_adamax_lr00001_fold5 / README.md
hkivancoral's picture
hkivancoral
End of training
6626728
preview code
|
raw history blame contribute delete
No virus
3.9 kB
metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_adamax_lr00001_fold5
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4634146341463415

hushem_1x_deit_tiny_adamax_lr00001_fold5

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2718
  • Accuracy: 0.4634

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: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.67 1 1.6411 0.1220
No log 2.0 3 1.5318 0.1951
No log 2.67 4 1.5063 0.1707
No log 4.0 6 1.4764 0.3171
No log 4.67 7 1.4625 0.3171
No log 6.0 9 1.4352 0.3902
1.379 6.67 10 1.4210 0.4390
1.379 8.0 12 1.3993 0.4390
1.379 8.67 13 1.3906 0.4390
1.379 10.0 15 1.3747 0.4146
1.379 10.67 16 1.3676 0.4146
1.379 12.0 18 1.3554 0.4146
1.379 12.67 19 1.3500 0.4146
1.107 14.0 21 1.3389 0.4146
1.107 14.67 22 1.3348 0.4146
1.107 16.0 24 1.3265 0.4390
1.107 16.67 25 1.3236 0.4634
1.107 18.0 27 1.3162 0.4634
1.107 18.67 28 1.3129 0.4390
0.9495 20.0 30 1.3051 0.4390
0.9495 20.67 31 1.3019 0.4390
0.9495 22.0 33 1.2961 0.4390
0.9495 22.67 34 1.2934 0.4634
0.9495 24.0 36 1.2879 0.4390
0.9495 24.67 37 1.2851 0.4390
0.9495 26.0 39 1.2815 0.4390
0.8401 26.67 40 1.2802 0.4390
0.8401 28.0 42 1.2775 0.4390
0.8401 28.67 43 1.2761 0.4390
0.8401 30.0 45 1.2740 0.4390
0.8401 30.67 46 1.2734 0.4390
0.8401 32.0 48 1.2723 0.4634
0.8401 32.67 49 1.2719 0.4634
0.7816 33.33 50 1.2718 0.4634

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1