metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_rms_001_fold3
    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: 0.7016666666666667

smids_3x_deit_tiny_rms_001_fold3

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: 0.7451
Accuracy: 0.7017

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
1.0251	1.0	225	0.9264	0.5433
0.8762	2.0	450	0.9033	0.5383
0.9046	3.0	675	0.8597	0.52
0.8168	4.0	900	0.8502	0.5867
0.8182	5.0	1125	0.8396	0.5417
0.8366	6.0	1350	0.8032	0.64
0.7873	7.0	1575	0.7872	0.6367
0.8222	8.0	1800	0.7892	0.605
0.8096	9.0	2025	0.8074	0.63
0.7866	10.0	2250	0.8155	0.5667
0.7895	11.0	2475	0.7692	0.6433
0.7721	12.0	2700	0.8106	0.6017
0.781	13.0	2925	0.7742	0.6533
0.7888	14.0	3150	0.7929	0.6117
0.7617	15.0	3375	0.7600	0.6683
0.8324	16.0	3600	0.7701	0.6433
0.7598	17.0	3825	0.8095	0.6333
0.7476	18.0	4050	0.7803	0.6033
0.7071	19.0	4275	0.7505	0.6683
0.7193	20.0	4500	0.7784	0.6183
0.6927	21.0	4725	0.7879	0.6467
0.666	22.0	4950	0.7212	0.6967
0.6763	23.0	5175	0.7194	0.6833
0.6715	24.0	5400	0.7919	0.6367
0.7294	25.0	5625	0.7785	0.6733
0.6936	26.0	5850	0.7216	0.6983
0.6322	27.0	6075	0.8000	0.6833
0.6761	28.0	6300	0.7942	0.6183
0.688	29.0	6525	0.7281	0.6567
0.6228	30.0	6750	0.7332	0.6567
0.6366	31.0	6975	0.7601	0.6717
0.6176	32.0	7200	0.7157	0.6883
0.6636	33.0	7425	0.7555	0.6567
0.6315	34.0	7650	0.7242	0.665
0.5915	35.0	7875	0.6940	0.6783
0.6259	36.0	8100	0.6760	0.6917
0.6325	37.0	8325	0.6834	0.6967
0.5846	38.0	8550	0.7137	0.6733
0.6018	39.0	8775	0.6801	0.6933
0.5692	40.0	9000	0.6837	0.6883
0.5234	41.0	9225	0.6917	0.6833
0.5543	42.0	9450	0.6614	0.7017
0.5363	43.0	9675	0.6720	0.7017
0.5474	44.0	9900	0.6703	0.7067
0.5234	45.0	10125	0.7035	0.6983
0.4923	46.0	10350	0.7111	0.7017
0.5435	47.0	10575	0.6985	0.7133
0.4932	48.0	10800	0.7085	0.7133
0.486	49.0	11025	0.7485	0.7033
0.4701	50.0	11250	0.7451	0.7017

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2