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_0001_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: 0.89

smids_3x_deit_tiny_rms_0001_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: 1.0441
Accuracy: 0.89

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: 0.0001
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
0.384	1.0	225	0.3552	0.8583
0.2084	2.0	450	0.3480	0.8767
0.201	3.0	675	0.4132	0.8717
0.1086	4.0	900	0.3792	0.8883
0.1243	5.0	1125	0.4370	0.8733
0.1107	6.0	1350	0.4615	0.8783
0.0664	7.0	1575	0.6966	0.865
0.0782	8.0	1800	0.6855	0.8733
0.0473	9.0	2025	0.5753	0.875
0.0605	10.0	2250	0.7929	0.87
0.0782	11.0	2475	0.6012	0.8933
0.0086	12.0	2700	0.6879	0.8817
0.0115	13.0	2925	0.8156	0.86
0.0467	14.0	3150	1.0598	0.8467
0.1265	15.0	3375	0.7615	0.875
0.0039	16.0	3600	0.7484	0.8767
0.0138	17.0	3825	0.8169	0.87
0.0039	18.0	4050	0.8702	0.8783
0.0077	19.0	4275	0.8767	0.8867
0.0399	20.0	4500	0.8253	0.8817
0.0266	21.0	4725	1.0317	0.8567
0.0092	22.0	4950	1.0021	0.8683
0.0011	23.0	5175	0.9409	0.8867
0.0242	24.0	5400	0.9565	0.8733
0.0188	25.0	5625	0.8702	0.88
0.0079	26.0	5850	0.8620	0.8783
0.009	27.0	6075	0.8382	0.8883
0.0334	28.0	6300	0.8240	0.885
0.0091	29.0	6525	0.9309	0.88
0.0428	30.0	6750	0.8520	0.8817
0.0064	31.0	6975	0.9518	0.8833
0.0205	32.0	7200	0.8143	0.8983
0.0358	33.0	7425	1.0040	0.8867
0.0085	34.0	7650	0.9891	0.88
0.0	35.0	7875	0.9233	0.9
0.0	36.0	8100	0.9033	0.8917
0.0025	37.0	8325	0.9886	0.895
0.001	38.0	8550	1.1074	0.87
0.0	39.0	8775	1.0071	0.8883
0.0	40.0	9000	1.0033	0.8883
0.0	41.0	9225	1.0288	0.8867
0.0	42.0	9450	1.0506	0.8833
0.0	43.0	9675	1.0220	0.8883
0.0	44.0	9900	1.0225	0.885
0.0	45.0	10125	1.0227	0.8867
0.0	46.0	10350	1.0356	0.8867
0.0027	47.0	10575	1.0358	0.8917
0.0	48.0	10800	1.0430	0.89
0.0	49.0	11025	1.0451	0.89
0.0	50.0	11250	1.0441	0.89

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2