metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_tiny_adamax_0001_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: 0.8735440931780366

smids_1x_deit_tiny_adamax_0001_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: 0.8884
Accuracy: 0.8735

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
0.4433	1.0	75	0.4087	0.8136
0.2718	2.0	150	0.3393	0.8636
0.1523	3.0	225	0.3549	0.8602
0.1126	4.0	300	0.4203	0.8785
0.1857	5.0	375	0.5016	0.8702
0.1676	6.0	450	0.6812	0.8403
0.0685	7.0	525	0.6011	0.8719
0.0238	8.0	600	0.6670	0.8686
0.0519	9.0	675	0.6013	0.8686
0.0386	10.0	750	0.7008	0.8719
0.0148	11.0	825	0.7193	0.8619
0.0007	12.0	900	0.7563	0.8752
0.0241	13.0	975	0.7693	0.8636
0.0307	14.0	1050	0.8760	0.8636
0.0115	15.0	1125	0.7808	0.8719
0.0035	16.0	1200	0.7588	0.8669
0.0001	17.0	1275	0.8971	0.8619
0.0102	18.0	1350	0.7909	0.8719
0.0001	19.0	1425	0.7984	0.8636
0.004	20.0	1500	0.8206	0.8669
0.0001	21.0	1575	0.8515	0.8752
0.0	22.0	1650	0.7887	0.8752
0.0	23.0	1725	0.9036	0.8719
0.0001	24.0	1800	0.8151	0.8735
0.0	25.0	1875	0.8674	0.8669
0.0	26.0	1950	0.8463	0.8702
0.0044	27.0	2025	0.8541	0.8669
0.0043	28.0	2100	0.8322	0.8669
0.002	29.0	2175	0.8405	0.8686
0.0043	30.0	2250	0.8433	0.8686
0.0034	31.0	2325	0.8353	0.8752
0.0	32.0	2400	0.8744	0.8702
0.0	33.0	2475	0.8688	0.8669
0.0	34.0	2550	0.8557	0.8669
0.0091	35.0	2625	0.8746	0.8669
0.0	36.0	2700	0.8586	0.8686
0.0	37.0	2775	0.8715	0.8702
0.0	38.0	2850	0.8844	0.8719
0.0019	39.0	2925	0.8957	0.8735
0.0	40.0	3000	0.8803	0.8752
0.0031	41.0	3075	0.8802	0.8752
0.0	42.0	3150	0.8828	0.8752
0.0026	43.0	3225	0.8803	0.8735
0.003	44.0	3300	0.8878	0.8752
0.0	45.0	3375	0.8894	0.8702
0.0	46.0	3450	0.8874	0.8735
0.0024	47.0	3525	0.8958	0.8752
0.0	48.0	3600	0.8893	0.8735
0.0023	49.0	3675	0.8880	0.8735
0.0021	50.0	3750	0.8884	0.8735

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0