metadata

base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: dit-base-finetuned-rvlcdip
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.87
          - name: Precision
            type: precision
            value: 0.7623411371237458
          - name: Recall
            type: recall
            value: 0.87

dit-base-finetuned-rvlcdip

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: 0.5940
Accuracy: 0.87
Precision: 0.7623
Recall: 0.87
F1 Score: 0.8126

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 Score
No log	1.0	4	0.6006	0.8708	0.7584	0.8708	0.8107
No log	2.0	8	0.5169	0.8708	0.7584	0.8708	0.8107
No log	3.0	12	0.4027	0.8708	0.7584	0.8708	0.8107
0.5427	4.0	16	0.3865	0.8708	0.7584	0.8708	0.8107
0.5427	5.0	20	0.3894	0.8708	0.7584	0.8708	0.8107
0.5427	6.0	24	0.3729	0.8708	0.7584	0.8708	0.8107
0.5427	7.0	28	0.3707	0.8708	0.7584	0.8708	0.8107
0.4458	8.0	32	0.3790	0.8708	0.7584	0.8708	0.8107
0.4458	9.0	36	0.3504	0.8708	0.7584	0.8708	0.8107
0.4458	10.0	40	0.3356	0.8708	0.7584	0.8708	0.8107
0.4458	11.0	44	0.4082	0.8708	0.7584	0.8708	0.8107
0.4369	12.0	48	0.3455	0.8708	0.7584	0.8708	0.8107
0.4369	13.0	52	0.3074	0.8708	0.7584	0.8708	0.8107
0.4369	14.0	56	0.3097	0.8708	0.7584	0.8708	0.8107
0.4109	15.0	60	0.3173	0.8708	0.7584	0.8708	0.8107

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3