metadata

tags:
  - generated_from_trainer
datasets:
  - preprocessed1024_config
metrics:
  - accuracy
  - f1
model-index:
  - name: convnext-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: 0.5665829145728644
          - name: F1
            type: f1
            value:
              f1: 0.5549950963329491

convnext-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: 1.1801
Accuracy: {'accuracy': 0.5665829145728644}
F1: {'f1': 0.5549950963329491}

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
1.3412	1.0	796	1.1931	{'accuracy': 0.4547738693467337}	{'f1': 0.31154642522501674}
1.1149	2.0	1592	1.0845	{'accuracy': 0.4886934673366834}	{'f1': 0.40829339044510005}
1.0531	3.0	2388	1.1650	{'accuracy': 0.48304020100502515}	{'f1': 0.38992060973001436}
0.917	4.0	3184	0.9950	{'accuracy': 0.5452261306532663}	{'f1': 0.50281030200465}
0.8633	5.0	3980	1.0152	{'accuracy': 0.5552763819095478}	{'f1': 0.511332789082197}
0.7747	6.0	4776	1.0201	{'accuracy': 0.5703517587939698}	{'f1': 0.523154780871296}
0.7133	7.0	5572	1.0345	{'accuracy': 0.5640703517587939}	{'f1': 0.5198008328503952}
0.659	8.0	6368	1.0702	{'accuracy': 0.5785175879396985}	{'f1': 0.5460580312777853}
0.5943	9.0	7164	1.1634	{'accuracy': 0.5734924623115578}	{'f1': 0.5501266468657362}
0.5699	10.0	7960	1.1801	{'accuracy': 0.5665829145728644}	{'f1': 0.5549950963329491}

Framework versions

Transformers 4.20.1
Pytorch 1.12.0
Datasets 2.1.0
Tokenizers 0.12.1