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_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: 0.8036605657237936

smids_3x_deit_tiny_rms_001_fold2

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: 1.9828
Accuracy: 0.8037

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
0.8903	1.0	225	0.8221	0.5707
0.8376	2.0	450	0.7917	0.6023
0.7073	3.0	675	0.7643	0.5790
0.7158	4.0	900	0.7590	0.6506
0.6892	5.0	1125	0.7293	0.6506
0.6685	6.0	1350	0.6489	0.6755
0.606	7.0	1575	0.5908	0.7471
0.6652	8.0	1800	0.5586	0.7537
0.6311	9.0	2025	0.5971	0.7504
0.5645	10.0	2250	0.5700	0.7571
0.6575	11.0	2475	0.6329	0.7338
0.5727	12.0	2700	0.5598	0.7521
0.5704	13.0	2925	0.5359	0.7754
0.5215	14.0	3150	0.5587	0.7571
0.583	15.0	3375	0.5430	0.7787
0.5848	16.0	3600	0.5789	0.7255
0.5726	17.0	3825	0.5236	0.7854
0.489	18.0	4050	0.4992	0.8003
0.4753	19.0	4275	0.5863	0.7621
0.5263	20.0	4500	0.5219	0.7704
0.4735	21.0	4725	0.4900	0.7970
0.5712	22.0	4950	0.5240	0.8070
0.5071	23.0	5175	0.5142	0.7854
0.5079	24.0	5400	0.5416	0.7887
0.4923	25.0	5625	0.5063	0.8053
0.3927	26.0	5850	0.5633	0.7870
0.3745	27.0	6075	0.6375	0.7621
0.3879	28.0	6300	0.5787	0.7787
0.3817	29.0	6525	0.5796	0.7887
0.3478	30.0	6750	0.5474	0.8070
0.3866	31.0	6975	0.5776	0.7920
0.3993	32.0	7200	0.5703	0.8103
0.2926	33.0	7425	0.6089	0.7970
0.2772	34.0	7650	0.6185	0.8020
0.2754	35.0	7875	0.6777	0.7854
0.2402	36.0	8100	0.7422	0.7870
0.2412	37.0	8325	0.7873	0.7754
0.2165	38.0	8550	0.8318	0.8070
0.1829	39.0	8775	0.8522	0.7920
0.1669	40.0	9000	0.9913	0.7903
0.135	41.0	9225	1.0029	0.8003
0.1443	42.0	9450	1.0999	0.7987
0.0893	43.0	9675	1.2541	0.8020
0.0533	44.0	9900	1.3854	0.8003
0.065	45.0	10125	1.6713	0.7754
0.0332	46.0	10350	1.6089	0.8003
0.0177	47.0	10575	1.8304	0.7920
0.0017	48.0	10800	1.9277	0.7937
0.0154	49.0	11025	1.9904	0.8053
0.0079	50.0	11250	1.9828	0.8037

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2