metadata

license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_beit_base_adamax_0001_fold3
    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: 0.91

smids_3x_beit_base_adamax_0001_fold3

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: 0.8017
Accuracy: 0.91

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
0.3218	1.0	225	0.3049	0.8783
0.1886	2.0	450	0.2841	0.8967
0.0936	3.0	675	0.2901	0.905
0.0347	4.0	900	0.3730	0.9067
0.0374	5.0	1125	0.5485	0.885
0.0265	6.0	1350	0.5296	0.8933
0.0077	7.0	1575	0.5235	0.9083
0.0372	8.0	1800	0.5900	0.915
0.0065	9.0	2025	0.6549	0.905
0.0316	10.0	2250	0.8684	0.88
0.0273	11.0	2475	0.6609	0.9133
0.0029	12.0	2700	0.7388	0.8983
0.001	13.0	2925	0.7878	0.8933
0.0056	14.0	3150	0.7685	0.9
0.0005	15.0	3375	0.7882	0.8933
0.0052	16.0	3600	0.7154	0.9017
0.0026	17.0	3825	0.6645	0.8967
0.0001	18.0	4050	0.7811	0.8917
0.0005	19.0	4275	0.8281	0.8883
0.001	20.0	4500	0.6603	0.9083
0.0022	21.0	4725	0.7385	0.905
0.0001	22.0	4950	0.7367	0.9033
0.0002	23.0	5175	0.8135	0.885
0.0001	24.0	5400	0.8005	0.8983
0.0193	25.0	5625	0.8350	0.8883
0.0	26.0	5850	0.7434	0.905
0.0	27.0	6075	0.7179	0.905
0.0001	28.0	6300	0.8254	0.9
0.0047	29.0	6525	0.8746	0.8933
0.006	30.0	6750	0.8594	0.8983
0.0021	31.0	6975	0.8754	0.8917
0.0	32.0	7200	0.8773	0.8983
0.0	33.0	7425	0.8925	0.8867
0.0	34.0	7650	0.9018	0.89
0.0	35.0	7875	0.8530	0.8983
0.0	36.0	8100	0.7923	0.905
0.0	37.0	8325	0.7837	0.905
0.0	38.0	8550	0.8324	0.9017
0.0	39.0	8775	0.8536	0.895
0.0	40.0	9000	0.8379	0.8917
0.0009	41.0	9225	0.8179	0.8983
0.0	42.0	9450	0.8003	0.9067
0.0	43.0	9675	0.8131	0.9033
0.0	44.0	9900	0.7802	0.9117
0.0	45.0	10125	0.8111	0.9
0.0	46.0	10350	0.8012	0.9033
0.0026	47.0	10575	0.7937	0.9117
0.0	48.0	10800	0.8052	0.9117
0.0	49.0	11025	0.8066	0.9083
0.0	50.0	11250	0.8017	0.91

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2