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: hushem_5x_beit_base_rms_001_fold4
    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: 0.7619047619047619

hushem_5x_beit_base_rms_001_fold4

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: 1.5372
• Accuracy: 0.7619

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
1.4749	1.0	28	1.3999	0.2381
1.39	2.0	56	1.4010	0.2619
1.4057	3.0	84	1.3886	0.2381
1.3953	4.0	112	1.3773	0.2381
1.3855	5.0	140	1.3607	0.2619
1.3721	6.0	168	1.1238	0.5
1.2199	7.0	196	1.2305	0.4762
1.1505	8.0	224	0.9832	0.4762
1.1076	9.0	252	0.9145	0.5476
1.04	10.0	280	0.9689	0.5476
0.9947	11.0	308	0.8866	0.6429
1.0266	12.0	336	0.8639	0.6905
0.9955	13.0	364	0.8959	0.6190
0.9564	14.0	392	0.8608	0.6667
0.9123	15.0	420	0.7711	0.6905
0.9391	16.0	448	0.7070	0.7619
0.9117	17.0	476	0.7366	0.7619
0.902	18.0	504	0.7650	0.7143
0.8479	19.0	532	0.7181	0.7381
0.8138	20.0	560	0.8337	0.6667
0.7593	21.0	588	0.8325	0.6905
0.8558	22.0	616	0.7211	0.8095
0.8609	23.0	644	0.7758	0.7619
0.7997	24.0	672	0.8535	0.7143
0.6915	25.0	700	0.8962	0.7381
0.7445	26.0	728	0.7116	0.7619
0.6818	27.0	756	0.9464	0.5714
0.6812	28.0	784	0.6802	0.7143
0.662	29.0	812	1.0464	0.5476
0.6161	30.0	840	0.7154	0.7857
0.5942	31.0	868	0.6122	0.7619
0.571	32.0	896	0.6263	0.7857
0.5357	33.0	924	0.8564	0.8095
0.4815	34.0	952	0.9986	0.7381
0.5261	35.0	980	0.9173	0.8095
0.3508	36.0	1008	1.0846	0.7619
0.3469	37.0	1036	0.9412	0.8333
0.3024	38.0	1064	0.9602	0.8333
0.2908	39.0	1092	1.1234	0.8333
0.2222	40.0	1120	1.1275	0.8095
0.2149	41.0	1148	1.4618	0.7381
0.2207	42.0	1176	1.3470	0.7857
0.094	43.0	1204	1.5389	0.7619
0.1227	44.0	1232	1.3819	0.7857
0.0713	45.0	1260	1.5287	0.7619
0.0383	46.0	1288	1.5676	0.8095
0.0259	47.0	1316	1.4966	0.7857
0.023	48.0	1344	1.5355	0.7619
0.0304	49.0	1372	1.5372	0.7619
0.0233	50.0	1400	1.5372	0.7619

Framework versions

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