metadata

base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: dit-base-finetuned-rvlcdip-finetuned-ind-17-imbalanced-aadhaarmask
    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: 0.8458918688803746
          - name: Recall
            type: recall
            value: 0.8458918688803746
          - name: F1
            type: f1
            value: 0.8445087759723635
          - name: Precision
            type: precision
            value: 0.8462519380607423

dit-base-finetuned-rvlcdip-finetuned-ind-17-imbalanced-aadhaarmask

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: 0.3727
Accuracy: 0.8459
Recall: 0.8459
F1: 0.8445
Precision: 0.8463

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
gradient_accumulation_steps: 4
total_train_batch_size: 32
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	Recall	F1	Precision
0.9625	0.9974	293	0.8121	0.7812	0.7812	0.7600	0.7620
0.7711	1.9983	587	0.5780	0.8135	0.8135	0.7960	0.7843
0.555	2.9991	881	0.4868	0.8255	0.8255	0.8133	0.8133
0.6008	4.0	1175	0.4475	0.8357	0.8357	0.8281	0.8253
0.5318	4.9974	1468	0.4478	0.8267	0.8267	0.8221	0.8254
0.3382	5.9983	1762	0.3946	0.8463	0.8463	0.8412	0.8427
0.4307	6.9991	2056	0.4083	0.8344	0.8344	0.8317	0.8362
0.4613	8.0	2350	0.3915	0.8442	0.8442	0.8429	0.8481
0.3247	8.9974	2643	0.3758	0.8421	0.8421	0.8402	0.8395
0.3965	9.9745	2930	0.3637	0.8484	0.8484	0.8466	0.8470

Framework versions

Transformers 4.40.1
Pytorch 2.2.0a0+81ea7a4
Datasets 2.19.0
Tokenizers 0.19.1