README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: finetuned-SwinT-Indian-Food-Classification-v3
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: Indian-Food-Images
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9436769394261424

finetuned-SwinT-Indian-Food-Classification-v3

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the Indian-Food-Images dataset. It achieves the following results on the evaluation set:

• Loss: 0.2910
• Accuracy: 0.9437

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: 0.0002
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9511	0.3	100	0.6092	0.8172
0.6214	0.6	200	0.4406	0.8672
0.7355	0.9	300	0.3665	0.8927
0.6078	1.2	400	0.3285	0.9065
0.439	1.5	500	0.3855	0.8916
0.3644	1.8	600	0.4082	0.8969
0.4748	2.1	700	0.3496	0.9022
0.3966	2.4	800	0.3626	0.8905
0.5799	2.7	900	0.4833	0.8767
0.2995	3.0	1000	0.3387	0.9044
0.3152	3.3	1100	0.3739	0.9097
0.3284	3.6	1200	0.4217	0.8916
0.3631	3.9	1300	0.4118	0.9044
0.219	4.2	1400	0.3721	0.9139
0.2874	4.5	1500	0.3030	0.9288
0.2819	4.8	1600	0.4056	0.9150
0.1755	5.11	1700	0.4039	0.9097
0.2462	5.41	1800	0.3550	0.9118
0.1737	5.71	1900	0.3444	0.9150
0.174	6.01	2000	0.3667	0.9160
0.1536	6.31	2100	0.3301	0.9288
0.0911	6.61	2200	0.3390	0.9299
0.0907	6.91	2300	0.2923	0.9288
0.0921	7.21	2400	0.3502	0.9256
0.1662	7.51	2500	0.3197	0.9341
0.0607	7.81	2600	0.3092	0.9362
0.111	8.11	2700	0.3146	0.9394
0.0588	8.41	2800	0.3069	0.9341
0.131	8.71	2900	0.2971	0.9405
0.1903	9.01	3000	0.3078	0.9384
0.2116	9.31	3100	0.3112	0.9341
0.1415	9.61	3200	0.2956	0.9405
0.1106	9.91	3300	0.2910	0.9437

Framework versions

• Transformers 4.21.2
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1