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_1x_beit_base_sgd_001_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: 0.84

smids_1x_beit_base_sgd_001_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: 0.3943
• Accuracy: 0.84

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
1.0814	1.0	75	1.0547	0.4367
0.8585	2.0	150	0.8397	0.63
0.7866	3.0	225	0.7414	0.6717
0.7611	4.0	300	0.6885	0.71
0.6598	5.0	375	0.6453	0.7433
0.6441	6.0	450	0.6118	0.7433
0.6302	7.0	525	0.5846	0.7667
0.58	8.0	600	0.5727	0.7717
0.6043	9.0	675	0.5453	0.7783
0.5566	10.0	750	0.5288	0.7917
0.5653	11.0	825	0.5189	0.795
0.5734	12.0	900	0.5084	0.8
0.526	13.0	975	0.4978	0.8067
0.5307	14.0	1050	0.4908	0.805
0.441	15.0	1125	0.4849	0.8167
0.4731	16.0	1200	0.4739	0.8183
0.4795	17.0	1275	0.4707	0.82
0.4461	18.0	1350	0.4612	0.8267
0.4796	19.0	1425	0.4551	0.82
0.4848	20.0	1500	0.4529	0.8217
0.4879	21.0	1575	0.4473	0.8217
0.424	22.0	1650	0.4423	0.8217
0.4384	23.0	1725	0.4358	0.8233
0.4332	24.0	1800	0.4326	0.83
0.4502	25.0	1875	0.4313	0.83
0.4747	26.0	1950	0.4225	0.83
0.4021	27.0	2025	0.4213	0.8367
0.3712	28.0	2100	0.4180	0.83
0.4664	29.0	2175	0.4162	0.835
0.4161	30.0	2250	0.4131	0.8317
0.3674	31.0	2325	0.4116	0.8383
0.3951	32.0	2400	0.4117	0.8317
0.4114	33.0	2475	0.4085	0.8333
0.3877	34.0	2550	0.4062	0.84
0.4073	35.0	2625	0.4053	0.8383
0.4212	36.0	2700	0.4054	0.8367
0.3625	37.0	2775	0.4024	0.8417
0.4369	38.0	2850	0.4016	0.8383
0.393	39.0	2925	0.3994	0.8417
0.3655	40.0	3000	0.3982	0.84
0.3762	41.0	3075	0.3980	0.84
0.3881	42.0	3150	0.3976	0.8417
0.4602	43.0	3225	0.3971	0.845
0.3865	44.0	3300	0.3963	0.845
0.4135	45.0	3375	0.3957	0.8433
0.3962	46.0	3450	0.3950	0.8467
0.3933	47.0	3525	0.3951	0.8433
0.3744	48.0	3600	0.3945	0.84
0.4098	49.0	3675	0.3944	0.84
0.3625	50.0	3750	0.3943	0.84

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0