metadata

license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: swinv2-base-patch4-window8-256-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: 0.8463175819497658
          - name: Recall
            type: recall
            value: 0.8463175819497658
          - name: F1
            type: f1
            value: 0.8463640211224454
          - name: Precision
            type: precision
            value: 0.8481964005333177

swinv2-base-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3463
Accuracy: 0.8463
Recall: 0.8463
F1: 0.8464
Precision: 0.8482

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
No log	0.9974	293	0.6222	0.7901	0.7901	0.7737	0.7747
No log	1.9983	587	0.4901	0.8063	0.8063	0.7998	0.8066
No log	2.9991	881	0.4374	0.8225	0.8225	0.8170	0.8356
No log	4.0	1175	0.4262	0.8340	0.8340	0.8270	0.8541
No log	4.9974	1468	0.4079	0.8310	0.8310	0.8290	0.8379
No log	5.9983	1762	0.4117	0.8370	0.8370	0.8361	0.8509
No log	6.9991	2056	0.3807	0.8370	0.8370	0.8361	0.8416
No log	8.0	2350	0.3419	0.8595	0.8595	0.8583	0.8609
No log	8.9974	2643	0.3628	0.8438	0.8438	0.8424	0.8448
0.4492	9.9745	2930	0.3638	0.8399	0.8399	0.8394	0.8410

Framework versions

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