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_sgd_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: 0.6023294509151415

smids_1x_deit_tiny_sgd_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: 0.8723
Accuracy: 0.6023

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
1.2913	1.0	75	1.2725	0.3394
1.2246	2.0	150	1.2089	0.3444
1.1634	3.0	225	1.1639	0.3561
1.1405	4.0	300	1.1319	0.3661
1.1027	5.0	375	1.1096	0.3727
1.1314	6.0	450	1.0924	0.3960
1.1132	7.0	525	1.0784	0.4193
1.0729	8.0	600	1.0662	0.4359
1.064	9.0	675	1.0549	0.4526
1.0806	10.0	750	1.0450	0.4576
1.063	11.0	825	1.0356	0.4692
1.0489	12.0	900	1.0265	0.4792
1.0267	13.0	975	1.0180	0.4942
0.9878	14.0	1050	1.0096	0.5042
1.01	15.0	1125	1.0018	0.5058
0.9915	16.0	1200	0.9940	0.5092
0.9952	17.0	1275	0.9865	0.5158
1.0114	18.0	1350	0.9793	0.5258
1.0011	19.0	1425	0.9723	0.5308
0.9762	20.0	1500	0.9654	0.5358
1.0144	21.0	1575	0.9587	0.5408
0.9349	22.0	1650	0.9525	0.5507
0.9869	23.0	1725	0.9462	0.5557
0.9417	24.0	1800	0.9404	0.5591
0.9277	25.0	1875	0.9347	0.5591
0.9227	26.0	1950	0.9293	0.5707
0.9725	27.0	2025	0.9242	0.5674
0.9104	28.0	2100	0.9193	0.5691
0.9618	29.0	2175	0.9147	0.5774
0.8904	30.0	2250	0.9103	0.5824
0.9175	31.0	2325	0.9062	0.5840
0.916	32.0	2400	0.9024	0.5857
0.8843	33.0	2475	0.8990	0.5874
0.9346	34.0	2550	0.8956	0.5923
0.8711	35.0	2625	0.8924	0.5957
0.8808	36.0	2700	0.8897	0.5973
0.9043	37.0	2775	0.8870	0.5990
0.9738	38.0	2850	0.8847	0.5957
0.8643	39.0	2925	0.8826	0.5940
0.8918	40.0	3000	0.8806	0.5957
0.9135	41.0	3075	0.8789	0.5973
0.9187	42.0	3150	0.8773	0.5973
0.8721	43.0	3225	0.8760	0.5973
0.9047	44.0	3300	0.8749	0.5990
0.8511	45.0	3375	0.8740	0.6007
0.8649	46.0	3450	0.8732	0.6007
0.8917	47.0	3525	0.8727	0.6023
0.8901	48.0	3600	0.8724	0.6023
0.883	49.0	3675	0.8723	0.6023
0.8823	50.0	3750	0.8723	0.6023

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0