metadata

license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-finetuned-ve-Ub200
    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: 0.47058823529411764

swinv2-finetuned-ve-Ub200

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

Loss: 1.5977
Accuracy: 0.4706

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	6	7.9891	0.0980
No log	2.0	13	7.4848	0.0980
No log	2.92	19	6.2378	0.0980
No log	4.0	26	4.8900	0.0980
No log	4.92	32	3.8155	0.0980
No log	6.0	39	2.7342	0.0980
No log	6.92	45	2.0612	0.0980
No log	8.0	52	1.5977	0.4706
No log	8.92	58	1.3671	0.4706
No log	10.0	65	1.2122	0.4706
No log	10.92	71	1.1823	0.4706
No log	12.0	78	1.1835	0.4706
No log	12.92	84	1.1838	0.4706
No log	14.0	91	1.1778	0.4706
No log	14.92	97	1.1769	0.4706
3.2267	16.0	104	1.1762	0.4706
3.2267	16.92	110	1.1758	0.4706
3.2267	18.0	117	1.1770	0.4706
3.2267	18.46	120	1.1771	0.4706

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0