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-tiny-patch4-window8-256-DMAE-da-4e-5
    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

swinv2-tiny-patch4-window8-256-DMAE-da-4e-5

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: 0.9664
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: 4e-05
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
1.446	0.96	11	1.6275	0.1087
1.4497	2.0	23	1.5550	0.1087
1.388	2.96	34	1.3769	0.3261
1.2755	4.0	46	1.2483	0.4130
1.1574	4.96	57	1.1545	0.4565
1.0826	6.0	69	1.0429	0.5
0.9124	6.96	80	0.9318	0.5652
0.8228	8.0	92	1.0362	0.5217
0.733	8.96	103	0.9699	0.5870
0.7086	10.0	115	0.8269	0.6522
0.6459	10.96	126	0.8168	0.6739
0.5793	12.0	138	1.0780	0.6087
0.5904	12.96	149	1.0166	0.5870
0.5155	14.0	161	0.8489	0.6304
0.4693	14.96	172	0.8454	0.6522
0.4928	16.0	184	0.8161	0.6739
0.4763	16.96	195	0.7666	0.7174
0.4354	18.0	207	0.8828	0.6957
0.3661	18.96	218	0.8782	0.6739
0.3652	20.0	230	0.9418	0.6739
0.3733	20.96	241	0.8963	0.7174
0.3473	22.0	253	0.9053	0.7174
0.2988	22.96	264	0.8318	0.7391
0.349	24.0	276	1.1129	0.6087
0.2963	24.96	287	1.0557	0.6304
0.3025	26.0	299	0.9567	0.7391
0.2676	26.96	310	1.0131	0.6739
0.2848	28.0	322	0.9576	0.6957
0.2757	28.96	333	0.9821	0.7174
0.2564	30.0	345	1.0166	0.6522
0.2635	30.96	356	0.9664	0.7609
0.2413	32.0	368	0.9894	0.7391
0.2321	32.96	379	1.0272	0.7391
0.2517	34.0	391	1.0312	0.7174
0.2161	34.96	402	1.0433	0.7174
0.2304	36.0	414	1.0158	0.7174
0.2194	36.96	425	1.0120	0.6957
0.2395	38.0	437	1.0153	0.6957
0.2199	38.26	440	1.0149	0.6957

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0