README.md

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_001_fold1
    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: 0.8848080133555927

smids_3x_beit_base_adamax_001_fold1

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: 0.9792
• Accuracy: 0.8848

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
0.5347	1.0	226	0.5865	0.7796
0.481	2.0	452	0.4735	0.8047
0.392	3.0	678	0.3827	0.8397
0.3513	4.0	904	0.4550	0.8080
0.3191	5.0	1130	0.5279	0.8364
0.2659	6.0	1356	0.3980	0.8564
0.2461	7.0	1582	0.3991	0.8798
0.2656	8.0	1808	0.4588	0.8664
0.1595	9.0	2034	0.4089	0.8715
0.1456	10.0	2260	0.4772	0.8631
0.0575	11.0	2486	0.5294	0.8614
0.0953	12.0	2712	0.4940	0.8748
0.0784	13.0	2938	0.5992	0.8548
0.0313	14.0	3164	0.5155	0.8731
0.1006	15.0	3390	0.5131	0.8898
0.0394	16.0	3616	0.6916	0.8815
0.0372	17.0	3842	0.6693	0.8748
0.0368	18.0	4068	0.7021	0.8765
0.0584	19.0	4294	0.7487	0.8715
0.0031	20.0	4520	0.6697	0.8865
0.0088	21.0	4746	0.7746	0.8865
0.0319	22.0	4972	0.7417	0.8614
0.0133	23.0	5198	0.9026	0.8581
0.001	24.0	5424	0.7822	0.8865
0.0186	25.0	5650	0.8476	0.8698
0.0405	26.0	5876	0.7548	0.8915
0.0061	27.0	6102	0.7539	0.8798
0.0213	28.0	6328	0.8310	0.8848
0.0063	29.0	6554	0.7841	0.8781
0.0003	30.0	6780	0.8782	0.8798
0.0005	31.0	7006	0.8431	0.8865
0.0002	32.0	7232	0.8900	0.8915
0.0077	33.0	7458	0.9508	0.8898
0.0001	34.0	7684	0.8836	0.8848
0.0001	35.0	7910	0.8853	0.8898
0.0002	36.0	8136	0.8931	0.8865
0.0	37.0	8362	0.9183	0.8831
0.0	38.0	8588	0.9668	0.8865
0.0	39.0	8814	0.9612	0.8881
0.0002	40.0	9040	0.9819	0.8848
0.0033	41.0	9266	0.9561	0.8915
0.0038	42.0	9492	0.9632	0.8915
0.0001	43.0	9718	0.9739	0.8865
0.0	44.0	9944	0.9696	0.8848
0.0	45.0	10170	0.9928	0.8815
0.0	46.0	10396	0.9848	0.8798
0.0	47.0	10622	0.9849	0.8815
0.0	48.0	10848	0.9754	0.8831
0.0	49.0	11074	0.9791	0.8848
0.0	50.0	11300	0.9792	0.8848

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2