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hushem_40x_deit_small_sgd_001_fold1

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_40x_deit_small_sgd_001_fold1
    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: 0.7777777777777778

hushem_40x_deit_small_sgd_001_fold1

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

  • Loss: 0.6398
  • Accuracy: 0.7778

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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • 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
1.1952 1.0 215 1.4276 0.3778
1.0042 2.0 430 1.4162 0.2889
0.8488 3.0 645 1.3544 0.3778
0.7465 4.0 860 1.2674 0.4889
0.6036 5.0 1075 1.1735 0.5111
0.5355 6.0 1290 1.0934 0.5556
0.4712 7.0 1505 1.0208 0.6
0.3805 8.0 1720 0.9372 0.6222
0.3422 9.0 1935 0.8901 0.6444
0.2964 10.0 2150 0.8433 0.6667
0.2485 11.0 2365 0.7909 0.6889
0.2255 12.0 2580 0.7693 0.7111
0.1717 13.0 2795 0.7309 0.7556
0.1588 14.0 3010 0.7252 0.7556
0.1672 15.0 3225 0.6986 0.7333
0.1097 16.0 3440 0.6863 0.7333
0.1167 17.0 3655 0.6753 0.7556
0.0952 18.0 3870 0.6754 0.7556
0.0806 19.0 4085 0.6768 0.7556
0.0794 20.0 4300 0.6533 0.7556
0.0649 21.0 4515 0.6553 0.7556
0.0639 22.0 4730 0.6451 0.7556
0.0578 23.0 4945 0.6498 0.7556
0.0439 24.0 5160 0.6457 0.7556
0.0437 25.0 5375 0.6423 0.7556
0.038 26.0 5590 0.6342 0.7556
0.0346 27.0 5805 0.6184 0.7556
0.0278 28.0 6020 0.6299 0.7556
0.035 29.0 6235 0.6381 0.7556
0.0226 30.0 6450 0.6272 0.7556
0.0178 31.0 6665 0.6325 0.7556
0.019 32.0 6880 0.6409 0.7556
0.0184 33.0 7095 0.6323 0.7778
0.0238 34.0 7310 0.6091 0.7556
0.0126 35.0 7525 0.6363 0.7778
0.0156 36.0 7740 0.6253 0.7556
0.0165 37.0 7955 0.6280 0.7556
0.0106 38.0 8170 0.6294 0.7778
0.0189 39.0 8385 0.6262 0.7778
0.0098 40.0 8600 0.6454 0.7556
0.0098 41.0 8815 0.6342 0.7778
0.0112 42.0 9030 0.6356 0.7778
0.0128 43.0 9245 0.6416 0.7778
0.0115 44.0 9460 0.6374 0.7778
0.0087 45.0 9675 0.6423 0.7778
0.0077 46.0 9890 0.6446 0.7778
0.0087 47.0 10105 0.6388 0.7778
0.0071 48.0 10320 0.6394 0.7778
0.0087 49.0 10535 0.6404 0.7778
0.0108 50.0 10750 0.6398 0.7778

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2