metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_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: 0.875

smids_3x_deit_small_sgd_001_fold5

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: 0.2955
Accuracy: 0.875

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
0.8786	1.0	225	0.8329	0.6917
0.6756	2.0	450	0.6561	0.7467
0.5645	3.0	675	0.5574	0.7867
0.4671	4.0	900	0.4924	0.8067
0.3977	5.0	1125	0.4538	0.82
0.4177	6.0	1350	0.4235	0.8367
0.3878	7.0	1575	0.4039	0.8417
0.4378	8.0	1800	0.3874	0.8433
0.3622	9.0	2025	0.3772	0.8483
0.345	10.0	2250	0.3683	0.8517
0.3638	11.0	2475	0.3631	0.8533
0.3441	12.0	2700	0.3527	0.8583
0.3313	13.0	2925	0.3447	0.865
0.2901	14.0	3150	0.3405	0.8633
0.2288	15.0	3375	0.3333	0.865
0.3024	16.0	3600	0.3306	0.865
0.2544	17.0	3825	0.3278	0.8683
0.299	18.0	4050	0.3253	0.8667
0.2662	19.0	4275	0.3235	0.8667
0.2847	20.0	4500	0.3172	0.8683
0.2132	21.0	4725	0.3164	0.8667
0.2384	22.0	4950	0.3131	0.8717
0.2264	23.0	5175	0.3102	0.8733
0.2574	24.0	5400	0.3121	0.8667
0.2327	25.0	5625	0.3088	0.8683
0.2687	26.0	5850	0.3062	0.8667
0.28	27.0	6075	0.3048	0.8667
0.2544	28.0	6300	0.3033	0.8683
0.2339	29.0	6525	0.3018	0.87
0.2	30.0	6750	0.3023	0.8733
0.1716	31.0	6975	0.3008	0.8733
0.2152	32.0	7200	0.2995	0.8717
0.2129	33.0	7425	0.2994	0.8733
0.1758	34.0	7650	0.2988	0.875
0.1848	35.0	7875	0.3009	0.875
0.2108	36.0	8100	0.2991	0.875
0.2223	37.0	8325	0.2978	0.875
0.1689	38.0	8550	0.2975	0.8733
0.1768	39.0	8775	0.2974	0.8767
0.2093	40.0	9000	0.2965	0.8733
0.1994	41.0	9225	0.2966	0.8733
0.2309	42.0	9450	0.2956	0.8733
0.2412	43.0	9675	0.2974	0.8767
0.2229	44.0	9900	0.2958	0.875
0.2153	45.0	10125	0.2965	0.8767
0.1978	46.0	10350	0.2959	0.8767
0.2092	47.0	10575	0.2956	0.875
0.2126	48.0	10800	0.2958	0.875
0.2109	49.0	11025	0.2956	0.875
0.1728	50.0	11250	0.2955	0.875

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2