metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: >-
      vit-base-patch16-224-in21k-laneclassifierasphaltconcrete-detectorVITmain50epochs
    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:
              accuracy: 1
          - name: F1
            type: f1
            value:
              f1: 1
          - name: Precision
            type: precision
            value:
              precision: 1
          - name: Recall
            type: recall
            value:
              recall: 1

vit-base-patch16-224-in21k-laneclassifierasphaltconcrete-detectorVITmain50epochs

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0004
Accuracy: {'accuracy': 1.0}
F1: {'f1': 1.0}
Precision: {'precision': 1.0}
Recall: {'recall': 1.0}

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0576	0.9933	111	0.0139	{'accuracy': 0.9977628635346756}	{'f1': 0.9966709613995368}	{'precision': 0.9985795454545454}	{'recall': 0.9947916666666667}
0.0365	1.9955	223	0.0012	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0009	2.9978	335	0.0008	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0007	4.0	447	0.0007	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0006	4.9933	558	0.0005	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0005	5.9955	670	0.0005	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0005	6.9978	782	0.0004	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0005	7.9463	888	0.0004	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}

Framework versions

Transformers 4.43.3
Pytorch 2.3.1
Datasets 2.20.0
Tokenizers 0.19.1