metadata

library_name: transformers
license: apache-2.0
base_model: facebook/vit-msn-small
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-msn-small-wbc-classifier-0316-cleaned-dataset-10
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.893532776066872

vit-msn-small-wbc-classifier-0316-cleaned-dataset-10

This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3538
Accuracy: 0.8935

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: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5193	1.0	16	0.6823	0.7945
0.5339	2.0	32	0.4553	0.8438
0.4778	3.0	48	0.4525	0.8478
0.4253	4.0	64	0.4077	0.8473
0.4086	5.0	80	0.4218	0.8575
0.3673	6.0	96	0.4002	0.8693
0.3275	7.0	112	0.3302	0.8773
0.3231	8.0	128	0.3672	0.8803
0.302	9.0	144	0.3363	0.8900
0.3122	10.0	160	0.3284	0.8843
0.2686	11.0	176	0.3317	0.8874
0.2786	12.0	192	0.3660	0.8883
0.2338	13.0	208	0.3520	0.8834
0.2466	14.0	224	0.3414	0.8896
0.2296	15.0	240	0.3531	0.8874
0.1961	16.0	256	0.3844	0.8847
0.2056	17.0	272	0.3705	0.8900
0.197	18.0	288	0.3538	0.8935
0.1748	19.0	304	0.3717	0.8887
0.1807	20.0	320	0.4075	0.8843
0.177	21.0	336	0.3881	0.8830
0.1433	22.0	352	0.4014	0.8856
0.1522	23.0	368	0.3918	0.8874
0.1322	24.0	384	0.4199	0.8905
0.1396	25.0	400	0.4142	0.8896

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1