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_fold2
    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: 0.8444444444444444

hushem_1x_beit_base_rms_00001_fold2

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: 0.9358
• Accuracy: 0.8444

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	1.2856	0.4889
1.4398	2.0	12	0.9696	0.6222
1.4398	3.0	18	0.7405	0.7111
0.463	4.0	24	0.8561	0.7333
0.1243	5.0	30	0.6572	0.8222
0.1243	6.0	36	0.6983	0.8444
0.0205	7.0	42	0.7294	0.8222
0.0205	8.0	48	0.6504	0.8
0.0064	9.0	54	0.6828	0.8222
0.0142	10.0	60	0.6539	0.8222
0.0142	11.0	66	0.7615	0.8444
0.0032	12.0	72	0.8146	0.8444
0.0032	13.0	78	0.8154	0.8444
0.0019	14.0	84	0.7947	0.8444
0.0028	15.0	90	0.7939	0.8444
0.0028	16.0	96	0.8240	0.8444
0.0013	17.0	102	0.8242	0.8222
0.0013	18.0	108	0.8443	0.8444
0.0014	19.0	114	0.8393	0.8444
0.0012	20.0	120	0.9165	0.8222
0.0012	21.0	126	0.8985	0.8222
0.0008	22.0	132	0.9053	0.8222
0.0008	23.0	138	0.9182	0.8222
0.0007	24.0	144	0.9131	0.8222
0.0007	25.0	150	0.9205	0.8222
0.0007	26.0	156	0.9165	0.8222
0.0004	27.0	162	0.9119	0.8222
0.0004	28.0	168	0.9185	0.8222
0.0005	29.0	174	0.9203	0.8222
0.0004	30.0	180	0.9232	0.8222
0.0004	31.0	186	0.9207	0.8444
0.0009	32.0	192	0.9256	0.8444
0.0009	33.0	198	0.9230	0.8444
0.0082	34.0	204	0.9200	0.8444
0.0007	35.0	210	0.9385	0.8444
0.0007	36.0	216	0.9350	0.8444
0.0005	37.0	222	0.9367	0.8444
0.0005	38.0	228	0.9290	0.8444
0.0044	39.0	234	0.9294	0.8444
0.0005	40.0	240	0.9330	0.8444
0.0005	41.0	246	0.9359	0.8444
0.0006	42.0	252	0.9358	0.8444
0.0006	43.0	258	0.9358	0.8444
0.0005	44.0	264	0.9358	0.8444
0.0007	45.0	270	0.9358	0.8444
0.0007	46.0	276	0.9358	0.8444
0.0007	47.0	282	0.9358	0.8444
0.0007	48.0	288	0.9358	0.8444
0.0006	49.0	294	0.9358	0.8444
0.0004	50.0	300	0.9358	0.8444

Framework versions

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