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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: dinov2-base-fa-disabled-finetuned-har
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9164021164021164

dinov2-base-fa-disabled-finetuned-har

This model is a fine-tuned version of facebook/dinov2-base on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3027
  • Accuracy: 0.9164

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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.8554 0.9910 83 0.5252 0.8323
0.8162 1.9940 167 0.4597 0.8598
0.7303 2.9970 251 0.4403 0.8587
0.5644 4.0 335 0.3922 0.8746
0.5672 4.9910 418 0.3784 0.8857
0.454 5.9940 502 0.3856 0.8831
0.4379 6.9970 586 0.3510 0.8889
0.3356 8.0 670 0.3187 0.9063
0.2877 8.9910 753 0.3209 0.9116
0.2717 9.9104 830 0.3027 0.9164

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1