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_fold5
    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: 0.6316666666666667

smids_10x_beit_large_sgd_00001_fold5

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: 0.8273
Accuracy: 0.6317

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
1.2351	1.0	750	1.2384	0.335
1.188	2.0	1500	1.2067	0.3417
1.1425	3.0	2250	1.1794	0.35
1.0663	4.0	3000	1.1549	0.36
1.0302	5.0	3750	1.1332	0.3733
1.0803	6.0	4500	1.1133	0.3783
1.0194	7.0	5250	1.0948	0.395
1.041	8.0	6000	1.0776	0.4133
0.958	9.0	6750	1.0617	0.4267
0.9328	10.0	7500	1.0465	0.44
0.9293	11.0	8250	1.0324	0.4533
0.9087	12.0	9000	1.0189	0.465
0.9387	13.0	9750	1.0063	0.4783
0.8996	14.0	10500	0.9944	0.4933
0.8606	15.0	11250	0.9830	0.5083
0.8536	16.0	12000	0.9723	0.5117
0.8222	17.0	12750	0.9621	0.5217
0.8298	18.0	13500	0.9525	0.53
0.9106	19.0	14250	0.9434	0.54
0.8462	20.0	15000	0.9347	0.5483
0.8209	21.0	15750	0.9265	0.5533
0.8393	22.0	16500	0.9186	0.5583
0.8236	23.0	17250	0.9111	0.565
0.8476	24.0	18000	0.9042	0.5717
0.7925	25.0	18750	0.8975	0.5733
0.8294	26.0	19500	0.8913	0.5817
0.7415	27.0	20250	0.8856	0.585
0.8113	28.0	21000	0.8800	0.585
0.8087	29.0	21750	0.8747	0.5833
0.8087	30.0	22500	0.8698	0.59
0.7723	31.0	23250	0.8652	0.595
0.7864	32.0	24000	0.8609	0.6033
0.7882	33.0	24750	0.8569	0.6083
0.7814	34.0	25500	0.8532	0.61
0.8053	35.0	26250	0.8498	0.6117
0.7759	36.0	27000	0.8467	0.6167
0.73	37.0	27750	0.8438	0.6167
0.8437	38.0	28500	0.8412	0.6183
0.7621	39.0	29250	0.8389	0.6183
0.719	40.0	30000	0.8367	0.6217
0.7491	41.0	30750	0.8348	0.625
0.7887	42.0	31500	0.8332	0.625
0.8254	43.0	32250	0.8317	0.625
0.7337	44.0	33000	0.8305	0.6267
0.7762	45.0	33750	0.8295	0.6283
0.7277	46.0	34500	0.8286	0.6317
0.7733	47.0	35250	0.8280	0.6317
0.7249	48.0	36000	0.8276	0.6317
0.7591	49.0	36750	0.8274	0.6317
0.7103	50.0	37500	0.8273	0.6317

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2