metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_beit_large_sgd_00001_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: 0.6483333333333333

smids_10x_beit_large_sgd_00001_fold4

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

Loss: 0.7900
Accuracy: 0.6483

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
1.2211	1.0	750	1.1931	0.3533
1.1323	2.0	1500	1.1612	0.365
1.184	3.0	2250	1.1336	0.37
1.1136	4.0	3000	1.1091	0.3817
0.9758	5.0	3750	1.0870	0.385
1.0842	6.0	4500	1.0669	0.3917
1.0165	7.0	5250	1.0484	0.4117
1.0062	8.0	6000	1.0310	0.43
1.0015	9.0	6750	1.0148	0.4367
0.9415	10.0	7500	0.9997	0.45
0.9588	11.0	8250	0.9856	0.4533
0.9674	12.0	9000	0.9724	0.47
0.9046	13.0	9750	0.9600	0.4733
0.9542	14.0	10500	0.9483	0.4867
0.8663	15.0	11250	0.9372	0.5
0.8717	16.0	12000	0.9268	0.51
0.7922	17.0	12750	0.9171	0.525
0.8562	18.0	13500	0.9078	0.535
0.9212	19.0	14250	0.8991	0.5433
0.8823	20.0	15000	0.8907	0.5567
0.8498	21.0	15750	0.8828	0.565
0.8335	22.0	16500	0.8754	0.575
0.8369	23.0	17250	0.8683	0.5867
0.8886	24.0	18000	0.8617	0.5917
0.8131	25.0	18750	0.8555	0.6
0.8107	26.0	19500	0.8497	0.605
0.7489	27.0	20250	0.8442	0.61
0.8154	28.0	21000	0.8390	0.6167
0.7935	29.0	21750	0.8341	0.62
0.7606	30.0	22500	0.8296	0.6267
0.7688	31.0	23250	0.8253	0.6283
0.755	32.0	24000	0.8214	0.63
0.8046	33.0	24750	0.8176	0.63
0.8193	34.0	25500	0.8142	0.6317
0.7668	35.0	26250	0.8110	0.635
0.7573	36.0	27000	0.8080	0.6367
0.7928	37.0	27750	0.8053	0.6417
0.792	38.0	28500	0.8028	0.6417
0.7917	39.0	29250	0.8007	0.645
0.7521	40.0	30000	0.7987	0.645
0.777	41.0	30750	0.7969	0.6483
0.7956	42.0	31500	0.7954	0.6483
0.8067	43.0	32250	0.7940	0.65
0.7335	44.0	33000	0.7929	0.65
0.7708	45.0	33750	0.7920	0.6483
0.74	46.0	34500	0.7912	0.6483
0.7222	47.0	35250	0.7906	0.6483
0.7572	48.0	36000	0.7902	0.6483
0.7909	49.0	36750	0.7900	0.6483
0.7055	50.0	37500	0.7900	0.6483

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2