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_fold4
    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: 0.87

smids_3x_deit_small_sgd_001_fold4

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: 0.3235
Accuracy: 0.87

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
0.834	1.0	225	0.8192	0.67
0.6262	2.0	450	0.6329	0.755
0.5137	3.0	675	0.5393	0.8
0.4726	4.0	900	0.4881	0.8117
0.4753	5.0	1125	0.4529	0.825
0.3563	6.0	1350	0.4306	0.8367
0.4027	7.0	1575	0.4125	0.8317
0.4582	8.0	1800	0.4008	0.8433
0.378	9.0	2025	0.3888	0.8417
0.3387	10.0	2250	0.3828	0.84
0.366	11.0	2475	0.3744	0.8433
0.3618	12.0	2700	0.3690	0.845
0.2883	13.0	2925	0.3626	0.845
0.2516	14.0	3150	0.3569	0.8533
0.2729	15.0	3375	0.3543	0.8517
0.2661	16.0	3600	0.3505	0.8567
0.2566	17.0	3825	0.3484	0.8567
0.2958	18.0	4050	0.3449	0.86
0.2763	19.0	4275	0.3442	0.86
0.2103	20.0	4500	0.3404	0.8633
0.2473	21.0	4725	0.3384	0.8633
0.246	22.0	4950	0.3367	0.865
0.2436	23.0	5175	0.3379	0.8617
0.2089	24.0	5400	0.3339	0.8667
0.2559	25.0	5625	0.3325	0.8667
0.2143	26.0	5850	0.3311	0.8667
0.2194	27.0	6075	0.3314	0.8667
0.2076	28.0	6300	0.3304	0.865
0.1951	29.0	6525	0.3306	0.8633
0.2173	30.0	6750	0.3289	0.87
0.2138	31.0	6975	0.3276	0.8667
0.1666	32.0	7200	0.3279	0.8683
0.2362	33.0	7425	0.3284	0.8667
0.2048	34.0	7650	0.3267	0.8683
0.1835	35.0	7875	0.3262	0.8717
0.2278	36.0	8100	0.3250	0.8683
0.2162	37.0	8325	0.3259	0.8683
0.2267	38.0	8550	0.3242	0.8717
0.2006	39.0	8775	0.3241	0.8683
0.2205	40.0	9000	0.3240	0.87
0.1797	41.0	9225	0.3251	0.8683
0.1988	42.0	9450	0.3237	0.87
0.2045	43.0	9675	0.3237	0.8717
0.2456	44.0	9900	0.3239	0.87
0.1971	45.0	10125	0.3237	0.87
0.2036	46.0	10350	0.3240	0.8683
0.1749	47.0	10575	0.3238	0.8683
0.1994	48.0	10800	0.3236	0.87
0.2429	49.0	11025	0.3236	0.87
0.2034	50.0	11250	0.3235	0.87

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2