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_adamax_00001_fold5
    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: 0.8048780487804879

hushem_5x_beit_base_adamax_00001_fold5

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: 0.6249
• Accuracy: 0.8049

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
1.2175	1.0	28	1.0638	0.5610
0.735	2.0	56	0.7985	0.6585
0.4163	3.0	84	0.6796	0.7561
0.279	4.0	112	0.5555	0.7805
0.1477	5.0	140	0.5113	0.7805
0.1105	6.0	168	0.4149	0.8049
0.0639	7.0	196	0.4515	0.8049
0.0449	8.0	224	0.4623	0.8049
0.0253	9.0	252	0.4728	0.8049
0.0316	10.0	280	0.4972	0.8049
0.0123	11.0	308	0.4732	0.8049
0.0128	12.0	336	0.4924	0.8049
0.0101	13.0	364	0.4570	0.8049
0.0111	14.0	392	0.4394	0.8049
0.0107	15.0	420	0.4434	0.8293
0.0064	16.0	448	0.5061	0.8049
0.0038	17.0	476	0.4264	0.8049
0.0038	18.0	504	0.4542	0.8049
0.0106	19.0	532	0.5345	0.8049
0.0043	20.0	560	0.5084	0.8049
0.0022	21.0	588	0.5182	0.8049
0.0136	22.0	616	0.4661	0.8049
0.005	23.0	644	0.4938	0.8293
0.0094	24.0	672	0.5151	0.8293
0.0106	25.0	700	0.5393	0.8049
0.0023	26.0	728	0.5196	0.8293
0.0018	27.0	756	0.5228	0.8293
0.0039	28.0	784	0.5509	0.8049
0.002	29.0	812	0.5472	0.8049
0.0023	30.0	840	0.5687	0.8049
0.0017	31.0	868	0.5888	0.8049
0.0023	32.0	896	0.5665	0.8049
0.0021	33.0	924	0.5478	0.8049
0.002	34.0	952	0.5621	0.8049
0.0027	35.0	980	0.5915	0.8049
0.0012	36.0	1008	0.6391	0.8049
0.0008	37.0	1036	0.6817	0.8049
0.0029	38.0	1064	0.6733	0.8049
0.0009	39.0	1092	0.6240	0.8049
0.0018	40.0	1120	0.6057	0.8049
0.0019	41.0	1148	0.6204	0.8049
0.0009	42.0	1176	0.6350	0.8049
0.0017	43.0	1204	0.6368	0.8049
0.006	44.0	1232	0.6329	0.8049
0.0022	45.0	1260	0.6324	0.8049
0.0014	46.0	1288	0.6308	0.8049
0.0013	47.0	1316	0.6209	0.8049
0.0019	48.0	1344	0.6248	0.8049
0.0007	49.0	1372	0.6249	0.8049
0.0012	50.0	1400	0.6249	0.8049

Framework versions

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