metadata

license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_beit_base_adamax_00001_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: 0.6888888888888889

hushem_5x_beit_base_adamax_00001_fold1

This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.3063
Accuracy: 0.6889

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: 1e-05
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
1.2914	1.0	27	1.3981	0.3111
0.7752	2.0	54	1.2587	0.4222
0.473	3.0	81	1.1213	0.6
0.3517	4.0	108	1.0654	0.5778
0.2036	5.0	135	0.9700	0.6222
0.1396	6.0	162	0.9127	0.6444
0.1055	7.0	189	1.0554	0.6222
0.0683	8.0	216	0.9132	0.6222
0.0509	9.0	243	1.0907	0.6222
0.0285	10.0	270	1.0220	0.6667
0.0302	11.0	297	0.9814	0.6667
0.0178	12.0	324	1.0288	0.6667
0.0215	13.0	351	0.9906	0.6667
0.0098	14.0	378	0.9906	0.6667
0.0094	15.0	405	0.9909	0.6667
0.0079	16.0	432	1.0583	0.6889
0.0176	17.0	459	1.0002	0.7111
0.0071	18.0	486	1.1076	0.7111
0.0077	19.0	513	1.2658	0.7111
0.0085	20.0	540	1.2202	0.7111
0.0042	21.0	567	1.1485	0.6889
0.0109	22.0	594	1.1833	0.6667
0.0017	23.0	621	1.2496	0.6667
0.0025	24.0	648	1.2268	0.6667
0.0049	25.0	675	1.1304	0.6889
0.0023	26.0	702	1.0752	0.6667
0.002	27.0	729	1.3029	0.6889
0.0019	28.0	756	1.1867	0.6444
0.0014	29.0	783	1.1802	0.7333
0.002	30.0	810	1.3660	0.7111
0.0126	31.0	837	1.3022	0.6889
0.002	32.0	864	1.3902	0.6667
0.0046	33.0	891	1.3937	0.6889
0.0019	34.0	918	1.3856	0.7333
0.0048	35.0	945	1.3752	0.6667
0.002	36.0	972	1.3963	0.6667
0.0009	37.0	999	1.3895	0.7111
0.001	38.0	1026	1.2536	0.6889
0.0016	39.0	1053	1.2991	0.6667
0.0008	40.0	1080	1.2492	0.6889
0.0031	41.0	1107	1.2808	0.6889
0.0025	42.0	1134	1.3015	0.6667
0.0032	43.0	1161	1.3785	0.7111
0.0006	44.0	1188	1.3466	0.6889
0.004	45.0	1215	1.3569	0.6667
0.0022	46.0	1242	1.3406	0.6444
0.0027	47.0	1269	1.3081	0.6889
0.0015	48.0	1296	1.3063	0.6889
0.002	49.0	1323	1.3063	0.6889
0.003	50.0	1350	1.3063	0.6889

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0