metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: beit-base-patch16-224-pt22k-ft22k-rim_one-new
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          type: rimonedl
          name: RIM ONE DL
          split: test
        metrics:
          - type: f1
            value: 0.9197860962566845
            name: F1
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          type: rim one
          name: RIMONEDL
          split: test
        metrics:
          - type: precision
            value: 0.9247311827956989
            name: precision
          - type: recall
            value: 0.9148936170212766
            name: Recall
          - type: accuracy
            value: 0.8972602739726028
            name: Accuracy
          - type: roc_auc
            value: 0.8901391162029461
            name: AUC

beit-base-patch16-224-pt22k-ft22k-rim_one-new

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.4550
Accuracy: 0.8767

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.73	2	0.2411	0.9178
No log	1.73	4	0.2182	0.8973
No log	2.73	6	0.3085	0.8973
No log	3.73	8	0.2794	0.8973
0.1392	4.73	10	0.2398	0.9110
0.1392	5.73	12	0.2925	0.8973
0.1392	6.73	14	0.2798	0.9110
0.1392	7.73	16	0.2184	0.9178
0.1392	8.73	18	0.3007	0.9110
0.0416	9.73	20	0.3344	0.9041
0.0416	10.73	22	0.3626	0.9110
0.0416	11.73	24	0.4842	0.8904
0.0416	12.73	26	0.3664	0.8973
0.0416	13.73	28	0.3458	0.9110
0.0263	14.73	30	0.2810	0.9110
0.0263	15.73	32	0.4695	0.8699
0.0263	16.73	34	0.3723	0.9041
0.0263	17.73	36	0.3447	0.9041
0.0263	18.73	38	0.3708	0.8904
0.0264	19.73	40	0.4052	0.9110
0.0264	20.73	42	0.4492	0.9041
0.0264	21.73	44	0.4649	0.8904
0.0264	22.73	46	0.4061	0.9178
0.0264	23.73	48	0.4136	0.9110
0.0139	24.73	50	0.4183	0.8973
0.0139	25.73	52	0.4504	0.8904
0.0139	26.73	54	0.4368	0.8973
0.0139	27.73	56	0.4711	0.9110
0.0139	28.73	58	0.3928	0.9110
0.005	29.73	60	0.4550	0.8767

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1