metadata

tags:
  - generated_from_trainer
datasets:
  - preprocessed1024_config
metrics:
  - accuracy
  - f1
model-index:
  - name: vit-mlo-512-breat_composition
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: preprocessed1024_config
          type: preprocessed1024_config
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value:
              accuracy: 0.5791457286432161
          - name: F1
            type: f1
            value:
              f1: 0.5749067914290308

vit-mlo-512-breat_composition

This model is a fine-tuned version of on the preprocessed1024_config dataset. It achieves the following results on the evaluation set:

Loss: 1.3123
Accuracy: {'accuracy': 0.5791457286432161}
F1: {'f1': 0.5749067914290308}

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.2679	1.0	796	1.0281	{'accuracy': 0.5062814070351759}	{'f1': 0.38950358034816535}
0.9805	2.0	1592	0.9240	{'accuracy': 0.5672110552763819}	{'f1': 0.5273112700912543}
0.9167	3.0	2388	0.9608	{'accuracy': 0.5477386934673367}	{'f1': 0.45736748568671376}
0.8292	4.0	3184	0.8973	{'accuracy': 0.5891959798994975}	{'f1': 0.5783349603036094}
0.7695	5.0	3980	1.0477	{'accuracy': 0.5571608040201005}	{'f1': 0.5379432393338944}
0.6912	6.0	4776	0.9479	{'accuracy': 0.585427135678392}	{'f1': 0.5766494177636581}
0.61	7.0	5572	1.1280	{'accuracy': 0.5703517587939698}	{'f1': 0.5560158679652624}
0.5591	8.0	6368	1.1866	{'accuracy': 0.5741206030150754}	{'f1': 0.5541999644498281}
0.5021	9.0	7164	1.1537	{'accuracy': 0.582286432160804}	{'f1': 0.566315815243799}
0.4262	10.0	7960	1.3123	{'accuracy': 0.5791457286432161}	{'f1': 0.5749067914290308}

Framework versions

Transformers 4.20.1
Pytorch 1.12.0
Datasets 2.1.0
Tokenizers 0.12.1