metadata

base_model: MBZUAI/swiftformer-xs
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: swiftformer-xs
    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: 0.57
          - name: Precision
            type: precision
            value: 0.59945
          - name: Recall
            type: recall
            value: 0.57

swiftformer-xs

This model is a fine-tuned version of MBZUAI/swiftformer-xs on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6833
Accuracy: 0.57
Precision: 0.5995
Recall: 0.57
F1 Score: 0.5828

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 Score
No log	1.0	4	0.6713	0.6292	0.6454	0.6292	0.6365
No log	2.0	8	0.7142	0.475	0.6155	0.475	0.5020
No log	3.0	12	0.7298	0.425	0.6026	0.425	0.4435
No log	4.0	16	0.7389	0.4792	0.6408	0.4792	0.5023
No log	5.0	20	0.7427	0.4792	0.6408	0.4792	0.5023
No log	6.0	24	0.7235	0.5083	0.6424	0.5083	0.5348
No log	7.0	28	0.6893	0.5875	0.6687	0.5875	0.6107
0.6981	8.0	32	0.6816	0.6042	0.6847	0.6042	0.6264
0.6981	9.0	36	0.6866	0.6042	0.6888	0.6042	0.6266
0.6981	10.0	40	0.7005	0.575	0.6751	0.575	0.5996
0.6981	11.0	44	0.7127	0.525	0.6554	0.525	0.5510
0.6981	12.0	48	0.7098	0.5333	0.6595	0.5333	0.5593
0.6981	13.0	52	0.7126	0.5208	0.6579	0.5208	0.5463
0.6981	14.0	56	0.7114	0.5292	0.6575	0.5292	0.5551
0.6656	15.0	60	0.6908	0.5667	0.6712	0.5667	0.5917
0.6656	16.0	64	0.6804	0.5833	0.6749	0.5833	0.6073
0.6656	17.0	68	0.6806	0.5958	0.6808	0.5958	0.6188
0.6656	18.0	72	0.6884	0.5583	0.6629	0.5583	0.5838
0.6656	19.0	76	0.6821	0.5708	0.6647	0.5708	0.5955
0.6656	20.0	80	0.6663	0.6042	0.6806	0.6042	0.6261
0.6656	21.0	84	0.6717	0.6	0.6787	0.6	0.6223
0.6656	22.0	88	0.6682	0.6083	0.6826	0.6083	0.6299
0.6443	23.0	92	0.6683	0.6167	0.6946	0.6167	0.6381
0.6443	24.0	96	0.6733	0.6	0.6911	0.6	0.6230
0.6443	25.0	100	0.6647	0.6083	0.6866	0.6083	0.6302
0.6443	26.0	104	0.6729	0.6083	0.6907	0.6083	0.6305
0.6443	27.0	108	0.6740	0.6042	0.6930	0.6042	0.6268
0.6443	28.0	112	0.6809	0.5917	0.6916	0.5917	0.6153
0.6443	29.0	116	0.6778	0.6042	0.7017	0.6042	0.6270
0.6313	30.0	120	0.6794	0.5958	0.6935	0.5958	0.6192

Framework versions

Transformers 4.33.3
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3