metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_tiny_sgd_001_fold5
    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: 0.88

smids_10x_deit_tiny_sgd_001_fold5

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: 0.2805
Accuracy: 0.88

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
0.5873	1.0	750	0.5427	0.8017
0.4134	2.0	1500	0.4078	0.8383
0.4003	3.0	2250	0.3567	0.8583
0.322	4.0	3000	0.3309	0.8733
0.3592	5.0	3750	0.3090	0.8767
0.2384	6.0	4500	0.3021	0.8717
0.2287	7.0	5250	0.2872	0.8833
0.2763	8.0	6000	0.2770	0.8883
0.301	9.0	6750	0.2801	0.89
0.2498	10.0	7500	0.2717	0.8933
0.2639	11.0	8250	0.2693	0.8967
0.2576	12.0	9000	0.2726	0.8967
0.2998	13.0	9750	0.2655	0.905
0.2222	14.0	10500	0.2676	0.8933
0.2757	15.0	11250	0.2607	0.8933
0.1644	16.0	12000	0.2662	0.91
0.2069	17.0	12750	0.2656	0.9033
0.2175	18.0	13500	0.2618	0.9067
0.2174	19.0	14250	0.2668	0.9
0.1626	20.0	15000	0.2708	0.8983
0.1772	21.0	15750	0.2632	0.9017
0.1739	22.0	16500	0.2644	0.9017
0.2129	23.0	17250	0.2644	0.8983
0.1768	24.0	18000	0.2642	0.8983
0.1436	25.0	18750	0.2692	0.8933
0.1864	26.0	19500	0.2647	0.8983
0.13	27.0	20250	0.2627	0.8967
0.1786	28.0	21000	0.2674	0.8967
0.1885	29.0	21750	0.2653	0.895
0.1896	30.0	22500	0.2757	0.8867
0.1887	31.0	23250	0.2629	0.8983
0.1377	32.0	24000	0.2703	0.89
0.1805	33.0	24750	0.2693	0.8917
0.1524	34.0	25500	0.2706	0.89
0.1113	35.0	26250	0.2737	0.8883
0.153	36.0	27000	0.2742	0.8867
0.1281	37.0	27750	0.2787	0.8817
0.112	38.0	28500	0.2764	0.885
0.1149	39.0	29250	0.2767	0.885
0.136	40.0	30000	0.2752	0.8833
0.1297	41.0	30750	0.2749	0.8867
0.1614	42.0	31500	0.2776	0.8833
0.1176	43.0	32250	0.2769	0.8817
0.1355	44.0	33000	0.2814	0.8817
0.1418	45.0	33750	0.2806	0.8833
0.1165	46.0	34500	0.2801	0.8817
0.1556	47.0	35250	0.2815	0.88
0.1322	48.0	36000	0.2803	0.8817
0.1369	49.0	36750	0.2803	0.8833
0.1026	50.0	37500	0.2805	0.88

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2