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_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: 0.9523809523809523

hushem_1x_beit_base_rms_00001_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: 0.2419
• Accuracy: 0.9524

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	1.3507	0.4286
1.4746	2.0	12	1.0121	0.5238
1.4746	3.0	18	0.4084	0.9048
0.4975	4.0	24	0.8867	0.6905
0.135	5.0	30	0.3643	0.9048
0.135	6.0	36	0.2799	0.9048
0.0217	7.0	42	0.2749	0.9286
0.0217	8.0	48	0.1461	0.9524
0.0073	9.0	54	0.2904	0.9286
0.003	10.0	60	0.2142	0.9762
0.003	11.0	66	0.2416	0.9048
0.0024	12.0	72	0.2155	0.9286
0.0024	13.0	78	0.1970	0.9524
0.0018	14.0	84	0.2474	0.9286
0.002	15.0	90	0.2996	0.9048
0.002	16.0	96	0.2243	0.9524
0.0011	17.0	102	0.2323	0.9524
0.0011	18.0	108	0.2007	0.9286
0.0019	19.0	114	0.2031	0.9286
0.0015	20.0	120	0.2492	0.9286
0.0015	21.0	126	0.2398	0.9286
0.0022	22.0	132	0.2207	0.9286
0.0022	23.0	138	0.2104	0.9286
0.001	24.0	144	0.2272	0.9524
0.0009	25.0	150	0.2107	0.9286
0.0009	26.0	156	0.2183	0.9524
0.0009	27.0	162	0.2098	0.9524
0.0009	28.0	168	0.2285	0.9524
0.0007	29.0	174	0.2209	0.9524
0.0007	30.0	180	0.2991	0.9524
0.0007	31.0	186	0.2929	0.9286
0.0008	32.0	192	0.2866	0.9286
0.0008	33.0	198	0.2902	0.9524
0.0007	34.0	204	0.2876	0.9524
0.0041	35.0	210	0.2290	0.9524
0.0041	36.0	216	0.2314	0.9524
0.0005	37.0	222	0.2320	0.9524
0.0005	38.0	228	0.2342	0.9524
0.0005	39.0	234	0.2418	0.9524
0.0012	40.0	240	0.2419	0.9524
0.0012	41.0	246	0.2420	0.9524
0.0006	42.0	252	0.2419	0.9524
0.0006	43.0	258	0.2419	0.9524
0.0007	44.0	264	0.2419	0.9524
0.0041	45.0	270	0.2419	0.9524
0.0041	46.0	276	0.2419	0.9524
0.0014	47.0	282	0.2419	0.9524
0.0014	48.0	288	0.2419	0.9524
0.0023	49.0	294	0.2419	0.9524
0.0004	50.0	300	0.2419	0.9524

Framework versions

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