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: 0.7608695652173914

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: 0.7654
Accuracy: 0.7609

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: 0.005
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	1.3706	0.5435
No log	2.0	7	1.1672	0.6304
1.2937	2.86	10	1.2248	0.6087
1.2937	4.0	14	2.6986	0.3478
1.2937	4.86	17	2.6008	0.4565
1.2355	6.0	21	1.8900	0.4130
1.2355	6.86	24	1.5958	0.5
1.2355	8.0	28	1.3847	0.5652
1.1548	8.86	31	0.8762	0.7174
1.1548	10.0	35	1.0871	0.5435
1.1548	10.86	38	0.9779	0.5435
1.0561	12.0	42	1.2454	0.5435
1.0561	12.86	45	1.2277	0.5652
1.0561	14.0	49	0.9527	0.6087
1.0546	14.86	52	0.8797	0.6304
1.0546	16.0	56	0.9479	0.6304
1.0546	16.86	59	0.8696	0.6739
0.9493	18.0	63	0.9348	0.6522
0.9493	18.86	66	0.9890	0.5435
0.9354	20.0	70	0.9073	0.5870
0.9354	20.86	73	0.8763	0.6304
0.9354	22.0	77	0.9592	0.6522
0.8791	22.86	80	0.8940	0.6522
0.8791	24.0	84	0.8165	0.6957
0.8791	24.86	87	0.8249	0.6957
0.8017	26.0	91	0.8946	0.6739
0.8017	26.86	94	0.8210	0.6957
0.8017	28.0	98	0.7654	0.7609
0.8532	28.86	101	0.7492	0.7391
0.8532	30.0	105	0.7725	0.6957
0.8532	30.86	108	0.7932	0.7174
0.8205	32.0	112	0.8129	0.7391
0.8205	32.86	115	0.8089	0.6957
0.8205	34.0	119	0.8151	0.6957
0.8112	34.29	120	0.8074	0.6957

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0