README.md

metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_deit_tiny_rms_0001_fold3
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8816666666666667

smids_1x_deit_tiny_rms_0001_fold3

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.0984
• Accuracy: 0.8817

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: 0.0001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7593	1.0	75	0.5650	0.795
0.4619	2.0	150	0.5773	0.74
0.3635	3.0	225	0.4403	0.85
0.3309	4.0	300	0.3368	0.865
0.2141	5.0	375	0.4183	0.86
0.1728	6.0	450	0.5130	0.8617
0.154	7.0	525	0.4040	0.8683
0.1331	8.0	600	0.5264	0.855
0.0922	9.0	675	0.7609	0.8467
0.0818	10.0	750	0.6611	0.8733
0.1085	11.0	825	0.6834	0.8783
0.0246	12.0	900	0.6979	0.8783
0.0878	13.0	975	0.9032	0.8433
0.1089	14.0	1050	0.7716	0.86
0.0143	15.0	1125	0.8328	0.8633
0.0139	16.0	1200	0.8299	0.86
0.0102	17.0	1275	0.9296	0.865
0.1127	18.0	1350	0.8373	0.8517
0.0195	19.0	1425	0.9545	0.87
0.0133	20.0	1500	1.0697	0.8617
0.0108	21.0	1575	1.2111	0.8333
0.0129	22.0	1650	1.0019	0.8667
0.0225	23.0	1725	1.0409	0.8567
0.0251	24.0	1800	1.1816	0.8467
0.0012	25.0	1875	1.1276	0.8633
0.003	26.0	1950	1.2613	0.8517
0.0173	27.0	2025	1.2463	0.8667
0.0413	28.0	2100	1.0810	0.8667
0.0002	29.0	2175	1.0875	0.8683
0.0257	30.0	2250	1.0511	0.86
0.0	31.0	2325	0.9064	0.895
0.0	32.0	2400	0.9226	0.8933
0.0	33.0	2475	0.9451	0.8933
0.0062	34.0	2550	0.9369	0.8917
0.0083	35.0	2625	0.9494	0.885
0.0047	36.0	2700	0.9547	0.885
0.0019	37.0	2775	0.9760	0.885
0.0	38.0	2850	1.0147	0.8883
0.0	39.0	2925	0.9604	0.885
0.0	40.0	3000	0.9768	0.8883
0.0	41.0	3075	0.9916	0.8817
0.003	42.0	3150	1.0329	0.8867
0.0028	43.0	3225	1.1065	0.8733
0.0	44.0	3300	1.0908	0.8817
0.0025	45.0	3375	1.0917	0.8817
0.0025	46.0	3450	1.0917	0.8817
0.0051	47.0	3525	1.0933	0.8817
0.0	48.0	3600	1.0955	0.8817
0.0	49.0	3675	1.0973	0.8817
0.0044	50.0	3750	1.0984	0.8817

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0