Hugging Face's logo

hkivancoral
/
smids_5x_deit_tiny_sgd_001_fold2

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
smids_5x_deit_tiny_sgd_001_fold2 / README.md
hkivancoral's picture
hkivancoral
End of training
d450b08
preview code
|
raw
history blame contribute delete
4.86 kB
metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_tiny_sgd_001_fold2
    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: 0.8685524126455907

smids_5x_deit_tiny_sgd_001_fold2

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: 0.3358
  • Accuracy: 0.8686

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
0.7754 1.0 375 0.7240 0.7271
0.5298 2.0 750 0.5482 0.7837
0.4453 3.0 1125 0.4761 0.8186
0.3233 4.0 1500 0.4354 0.8286
0.3301 5.0 1875 0.4115 0.8386
0.3179 6.0 2250 0.3924 0.8469
0.3101 7.0 2625 0.3803 0.8502
0.3266 8.0 3000 0.3685 0.8586
0.2663 9.0 3375 0.3605 0.8552
0.2805 10.0 3750 0.3550 0.8536
0.2677 11.0 4125 0.3495 0.8619
0.3046 12.0 4500 0.3461 0.8686
0.2173 13.0 4875 0.3409 0.8602
0.2384 14.0 5250 0.3398 0.8636
0.2681 15.0 5625 0.3343 0.8652
0.1901 16.0 6000 0.3336 0.8735
0.2623 17.0 6375 0.3353 0.8735
0.1865 18.0 6750 0.3314 0.8735
0.2003 19.0 7125 0.3309 0.8735
0.2713 20.0 7500 0.3280 0.8752
0.2017 21.0 7875 0.3298 0.8702
0.1863 22.0 8250 0.3281 0.8769
0.227 23.0 8625 0.3271 0.8769
0.1889 24.0 9000 0.3290 0.8752
0.1561 25.0 9375 0.3282 0.8752
0.2339 26.0 9750 0.3258 0.8752
0.2006 27.0 10125 0.3286 0.8802
0.1745 28.0 10500 0.3294 0.8719
0.1852 29.0 10875 0.3284 0.8719
0.1931 30.0 11250 0.3301 0.8702
0.1811 31.0 11625 0.3297 0.8735
0.1783 32.0 12000 0.3325 0.8702
0.1809 33.0 12375 0.3288 0.8769
0.1274 34.0 12750 0.3315 0.8652
0.1957 35.0 13125 0.3314 0.8702
0.1704 36.0 13500 0.3319 0.8686
0.1796 37.0 13875 0.3309 0.8686
0.1565 38.0 14250 0.3327 0.8702
0.1735 39.0 14625 0.3325 0.8686
0.1525 40.0 15000 0.3345 0.8669
0.1548 41.0 15375 0.3344 0.8735
0.1677 42.0 15750 0.3353 0.8669
0.1708 43.0 16125 0.3357 0.8669
0.1467 44.0 16500 0.3356 0.8669
0.1338 45.0 16875 0.3358 0.8686
0.2032 46.0 17250 0.3360 0.8669
0.1609 47.0 17625 0.3359 0.8686
0.155 48.0 18000 0.3359 0.8686
0.2258 49.0 18375 0.3359 0.8669
0.1319 50.0 18750 0.3358 0.8686

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2