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_1x_beit_base_rms_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: 0.8292682926829268

hushem_1x_beit_base_rms_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: 0.7273
• Accuracy: 0.8293

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
No log	1.0	6	1.3481	0.2927
1.3909	2.0	12	0.6605	0.7317
1.3909	3.0	18	0.4292	0.8780
0.5403	4.0	24	0.4452	0.7805
0.1465	5.0	30	0.3293	0.8537
0.1465	6.0	36	0.3638	0.8537
0.0351	7.0	42	0.4318	0.8537
0.0351	8.0	48	0.5448	0.8537
0.0141	9.0	54	0.6437	0.8049
0.0043	10.0	60	0.5878	0.8293
0.0043	11.0	66	0.6177	0.8537
0.0037	12.0	72	0.5464	0.8537
0.0037	13.0	78	0.5884	0.8537
0.0055	14.0	84	0.5978	0.8537
0.0023	15.0	90	0.6603	0.8293
0.0023	16.0	96	0.8364	0.7805
0.0022	17.0	102	0.7710	0.8049
0.0022	18.0	108	0.8111	0.7805
0.0021	19.0	114	0.8487	0.7805
0.0014	20.0	120	0.7148	0.8049
0.0014	21.0	126	0.7288	0.8049
0.0018	22.0	132	0.6188	0.8537
0.0018	23.0	138	0.6580	0.8537
0.0007	24.0	144	0.6927	0.8537
0.0009	25.0	150	0.6863	0.8537
0.0009	26.0	156	0.6891	0.8537
0.0005	27.0	162	0.7029	0.8537
0.0005	28.0	168	0.6879	0.8537
0.0008	29.0	174	0.7177	0.8537
0.0005	30.0	180	0.7192	0.8537
0.0005	31.0	186	0.6892	0.8537
0.0009	32.0	192	0.7016	0.8537
0.0009	33.0	198	0.6329	0.8537
0.0013	34.0	204	0.6550	0.8537
0.0012	35.0	210	0.7178	0.8293
0.0012	36.0	216	0.7226	0.8293
0.0005	37.0	222	0.7238	0.8293
0.0005	38.0	228	0.7249	0.8293
0.0004	39.0	234	0.7268	0.8293
0.0005	40.0	240	0.7276	0.8293
0.0005	41.0	246	0.7269	0.8293
0.0008	42.0	252	0.7273	0.8293
0.0008	43.0	258	0.7273	0.8293
0.0005	44.0	264	0.7273	0.8293
0.0004	45.0	270	0.7273	0.8293
0.0004	46.0	276	0.7273	0.8293
0.0071	47.0	282	0.7273	0.8293
0.0071	48.0	288	0.7273	0.8293
0.0008	49.0	294	0.7273	0.8293
0.0003	50.0	300	0.7273	0.8293

Framework versions

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