metadata

base_model: MBZUAI/swiftformer-xs
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swiftformer-xs-DMAE
    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: 0.7391304347826086

swiftformer-xs-DMAE

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: 0.8596
Accuracy: 0.7391

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: 0.0015
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	1.3836	0.4565
No log	2.0	7	1.3327	0.6522
1.3567	2.86	10	1.1681	0.6522
1.3567	4.0	14	1.0440	0.5652
1.3567	4.86	17	1.0462	0.6304
1.0903	6.0	21	0.9294	0.5870
1.0903	6.86	24	0.9572	0.6522
1.0903	8.0	28	0.9286	0.6739
1.0969	8.86	31	0.9229	0.6304
1.0969	10.0	35	0.9061	0.6522
1.0969	10.86	38	0.8341	0.6739
0.8923	12.0	42	0.8786	0.6739
0.8923	12.86	45	0.8596	0.7391
0.8923	14.0	49	0.8902	0.7174
0.7289	14.86	52	0.8024	0.6739
0.7289	16.0	56	0.9341	0.7174
0.7289	16.86	59	1.0464	0.7174
0.6609	18.0	63	0.9923	0.6087
0.6609	18.86	66	0.8225	0.7174
0.6527	20.0	70	0.8748	0.6957
0.6527	20.86	73	0.8052	0.6739
0.6527	22.0	77	0.8861	0.6957
0.493	22.86	80	0.9555	0.6957
0.493	24.0	84	1.0336	0.6739
0.493	24.86	87	0.9961	0.6957
0.4088	26.0	91	1.0400	0.6957
0.4088	26.86	94	1.0536	0.6957
0.4088	28.0	98	1.1388	0.6739
0.4047	28.86	101	1.2295	0.6522
0.4047	30.0	105	1.2627	0.6522
0.4047	30.86	108	1.2372	0.6739
0.3681	32.0	112	1.2919	0.6522
0.3681	32.86	115	1.2453	0.6522
0.3681	34.0	119	1.2612	0.6739
0.353	34.29	120	1.2611	0.6957

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0