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_rms_00001_fold2
    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: 0.8666666666666667

hushem_5x_beit_base_rms_00001_fold2

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: 0.8184
Accuracy: 0.8667

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
0.6865	1.0	27	0.7969	0.7556
0.1615	2.0	54	0.9353	0.7778
0.041	3.0	81	1.0745	0.6444
0.0119	4.0	108	1.0481	0.7333
0.0095	5.0	135	0.6063	0.8667
0.0013	6.0	162	0.6520	0.8444
0.0015	7.0	189	0.7604	0.8667
0.0013	8.0	216	0.7595	0.8444
0.0008	9.0	243	0.8299	0.8444
0.0008	10.0	270	0.6509	0.8444
0.0009	11.0	297	0.7989	0.8444
0.0002	12.0	324	0.8458	0.8444
0.0005	13.0	351	0.6321	0.8667
0.0002	14.0	378	0.6972	0.8444
0.0002	15.0	405	0.7426	0.8667
0.0005	16.0	432	0.9776	0.8
0.0023	17.0	459	1.0180	0.8
0.0003	18.0	486	1.1105	0.7778
0.0006	19.0	513	0.9919	0.7556
0.0002	20.0	540	1.0177	0.8
0.0012	21.0	567	0.9992	0.8444
0.0003	22.0	594	0.9760	0.8444
0.0047	23.0	621	0.9891	0.8
0.0061	24.0	648	0.9730	0.8222
0.0002	25.0	675	0.8247	0.8222
0.0001	26.0	702	0.8270	0.8667
0.0001	27.0	729	0.7978	0.8222
0.0	28.0	756	0.8136	0.8444
0.0001	29.0	783	0.8553	0.8444
0.0001	30.0	810	0.9423	0.8444
0.0001	31.0	837	0.9286	0.8222
0.0001	32.0	864	0.9464	0.8222
0.0002	33.0	891	0.8713	0.8444
0.0001	34.0	918	0.8762	0.8444
0.0001	35.0	945	0.9092	0.8667
0.0	36.0	972	0.9547	0.8444
0.0	37.0	999	0.9283	0.8444
0.0	38.0	1026	0.8639	0.8444
0.0001	39.0	1053	0.8477	0.8667
0.0	40.0	1080	0.8432	0.8667
0.0	41.0	1107	0.8325	0.8667
0.0	42.0	1134	0.7851	0.8667
0.0003	43.0	1161	0.7875	0.8667
0.0	44.0	1188	0.7888	0.8667
0.0001	45.0	1215	0.8006	0.8889
0.0001	46.0	1242	0.8075	0.8889
0.0001	47.0	1269	0.8158	0.8889
0.0	48.0	1296	0.8184	0.8667
0.0002	49.0	1323	0.8184	0.8667
0.0001	50.0	1350	0.8184	0.8667

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0