metadata

tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
model-index:
  - name: dit-base-finetuned-brs
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8823529411764706
          - name: F1
            type: f1
            value: 0.8571428571428571

dit-base-finetuned-brs

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

Loss: 0.8748
Accuracy: 0.8824
F1: 0.8571
Precision (ppv): 0.8571
Recall (sensitivity): 0.8571
Specificity: 0.9
Npv: 0.9
Auc: 0.8786

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision (ppv)	Recall (sensitivity)	Specificity	Npv	Auc
0.6624	6.25	100	0.5548	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.5201	12.49	200	0.4617	0.8824	0.8571	0.8571	0.8571	0.9	0.9	0.8786
0.5172	18.74	300	0.4249	0.8235	0.8000	0.75	0.8571	0.8	0.8889	0.8286
0.4605	24.98	400	0.3172	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.4894	31.25	500	0.4466	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.3694	37.49	600	0.5077	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.6172	43.74	700	0.5722	0.7647	0.7143	0.7143	0.7143	0.8	0.8	0.7571
0.3671	49.98	800	0.7006	0.7647	0.6667	0.8	0.5714	0.9	0.75	0.7357
0.4109	56.25	900	0.4410	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.3198	62.49	1000	0.7226	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.4283	68.74	1100	0.8089	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.3273	74.98	1200	0.9059	0.7647	0.6667	0.8	0.5714	0.9	0.75	0.7357
0.3237	81.25	1300	0.8520	0.8235	0.7692	0.8333	0.7143	0.9	0.8182	0.8071
0.2014	87.49	1400	0.9183	0.7647	0.6667	0.8	0.5714	0.9	0.75	0.7357
0.3204	93.74	1500	0.6769	0.8824	0.8571	0.8571	0.8571	0.9	0.9	0.8786
0.1786	99.98	1600	0.8748	0.8824	0.8571	0.8571	0.8571	0.9	0.9	0.8786

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1