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_001_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: 0.5777777777777777

hushem_5x_beit_base_adamax_001_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: 6.3078
Accuracy: 0.5778

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: 0.001
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
1.4226	1.0	27	1.4140	0.2667
1.2045	2.0	54	1.6573	0.2889
1.2481	3.0	81	1.5730	0.2889
1.322	4.0	108	1.5814	0.2889
0.9418	5.0	135	1.4941	0.4889
0.9982	6.0	162	1.2480	0.4222
0.9049	7.0	189	1.2328	0.4667
0.7944	8.0	216	1.2343	0.4889
0.9595	9.0	243	1.3356	0.4667
0.7289	10.0	270	1.3692	0.4889
0.696	11.0	297	1.4324	0.4444
0.7466	12.0	324	1.4783	0.4667
0.7646	13.0	351	1.3725	0.4889
0.6451	14.0	378	2.0057	0.5333
0.5784	15.0	405	2.4024	0.4444
0.5544	16.0	432	2.4151	0.5111
0.563	17.0	459	1.9054	0.5556
0.5213	18.0	486	3.0169	0.5333
0.551	19.0	513	2.4504	0.5333
0.613	20.0	540	2.7289	0.5333
0.4577	21.0	567	2.8661	0.5111
0.3823	22.0	594	2.7689	0.4444
0.3921	23.0	621	3.3303	0.5556
0.3974	24.0	648	3.5099	0.4444
0.3186	25.0	675	2.8023	0.5556
0.2983	26.0	702	3.0145	0.4889
0.2885	27.0	729	3.6675	0.4667
0.1902	28.0	756	3.2605	0.5556
0.2253	29.0	783	4.9420	0.5111
0.1963	30.0	810	4.0120	0.4889
0.1797	31.0	837	4.7762	0.5778
0.1892	32.0	864	4.0878	0.5333
0.1404	33.0	891	4.6569	0.5111
0.0882	34.0	918	4.6823	0.5556
0.1578	35.0	945	5.1512	0.5111
0.0782	36.0	972	5.2444	0.5778
0.0461	37.0	999	5.0650	0.5556
0.0253	38.0	1026	5.4464	0.5556
0.0617	39.0	1053	5.7436	0.5778
0.0131	40.0	1080	6.2467	0.5556
0.0373	41.0	1107	6.5043	0.5778
0.0018	42.0	1134	6.2715	0.5778
0.0403	43.0	1161	6.0713	0.5556
0.0098	44.0	1188	6.6508	0.5556
0.0159	45.0	1215	6.4236	0.5778
0.0031	46.0	1242	6.3525	0.5778
0.007	47.0	1269	6.2593	0.5778
0.0011	48.0	1296	6.3063	0.5778
0.0085	49.0	1323	6.3078	0.5778
0.0126	50.0	1350	6.3078	0.5778

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0