metadata

base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: dit-base-finetuned-rvlcdip-finetuned-mobile-eye-tracking-dataset-v2
    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: 0.8333333333333334

dit-base-finetuned-rvlcdip-finetuned-mobile-eye-tracking-dataset-v2

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

Loss: 0.4029
Accuracy: 0.8333

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	0.6939	0.4792
No log	2.0	7	0.6927	0.5417
0.6938	2.86	10	0.6986	0.4167
0.6938	4.0	14	0.6959	0.4583
0.6938	4.86	17	0.6798	0.6875
0.684	6.0	21	0.6890	0.5208
0.684	6.86	24	0.6768	0.5833
0.684	8.0	28	0.6637	0.6667
0.6772	8.86	31	0.7099	0.5
0.6772	10.0	35	0.6467	0.6042
0.6772	10.86	38	0.6059	0.6875
0.6283	12.0	42	0.5860	0.7292
0.6283	12.86	45	0.5670	0.7708
0.6283	14.0	49	0.5677	0.6875
0.5712	14.86	52	0.5453	0.6875
0.5712	16.0	56	0.4991	0.7708
0.5712	16.86	59	0.4830	0.75
0.5485	18.0	63	0.4485	0.7917
0.5485	18.86	66	0.4268	0.8542
0.506	20.0	70	0.4588	0.7708
0.506	20.86	73	0.4412	0.75
0.506	22.0	77	0.4099	0.8333
0.4636	22.86	80	0.4005	0.875
0.4636	24.0	84	0.4039	0.8333
0.4636	24.86	87	0.4040	0.8333
0.4453	25.71	90	0.4029	0.8333

Framework versions

Transformers 4.33.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.13.3