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_rms_001_fold1
    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: 0.7479131886477463

smids_1x_deit_tiny_rms_001_fold1

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: 1.2760
Accuracy: 0.7479

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
1.1876	1.0	76	1.3425	0.3356
1.1463	2.0	152	1.2111	0.3356
1.0954	3.0	228	1.0384	0.4908
0.9283	4.0	304	1.3266	0.3422
0.8828	5.0	380	0.8561	0.5609
0.9197	6.0	456	0.8658	0.5326
0.8712	7.0	532	0.8805	0.5242
0.8491	8.0	608	0.9865	0.5476
0.8029	9.0	684	0.8775	0.5609
0.7757	10.0	760	1.7456	0.4307
0.873	11.0	836	0.8686	0.5058
0.9608	12.0	912	0.7919	0.5893
0.7695	13.0	988	0.8046	0.5659
0.7753	14.0	1064	0.7823	0.5927
0.7166	15.0	1140	0.8383	0.5576
0.7186	16.0	1216	0.7798	0.6444
0.7445	17.0	1292	0.7722	0.6427
0.6602	18.0	1368	0.7838	0.6227
0.7252	19.0	1444	0.6881	0.6945
0.8232	20.0	1520	0.7249	0.6845
0.6331	21.0	1596	0.7098	0.6795
0.5883	22.0	1672	0.7178	0.6711
0.6215	23.0	1748	0.7089	0.6912
0.6142	24.0	1824	0.6619	0.7062
0.5494	25.0	1900	0.6904	0.7212
0.6135	26.0	1976	0.6508	0.7129
0.5009	27.0	2052	0.6608	0.7212
0.587	28.0	2128	0.7282	0.7112
0.4496	29.0	2204	0.6988	0.7095
0.4737	30.0	2280	0.7982	0.6728
0.5606	31.0	2356	0.7102	0.7062
0.5118	32.0	2432	0.6558	0.7479
0.4696	33.0	2508	0.9166	0.6978
0.5104	34.0	2584	0.7689	0.6995
0.4795	35.0	2660	0.6827	0.7362
0.432	36.0	2736	0.7633	0.7129
0.4887	37.0	2812	0.7070	0.7162
0.4102	38.0	2888	0.7227	0.7396
0.3613	39.0	2964	0.7184	0.7329
0.3288	40.0	3040	0.7694	0.7379
0.3339	41.0	3116	0.7145	0.7429
0.242	42.0	3192	0.8275	0.7429
0.3299	43.0	3268	0.8614	0.7446
0.2932	44.0	3344	0.9021	0.7362
0.2403	45.0	3420	0.9785	0.7479
0.1305	46.0	3496	1.0696	0.7546
0.1845	47.0	3572	1.1023	0.7529
0.1407	48.0	3648	1.2188	0.7513
0.0723	49.0	3724	1.2702	0.7479
0.0363	50.0	3800	1.2760	0.7479

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0