metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_adamax_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: 0.905

smids_3x_deit_small_adamax_0001_fold3

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

Loss: 0.8015
Accuracy: 0.905

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
0.3198	1.0	225	0.2665	0.8883
0.2096	2.0	450	0.2570	0.9017
0.0616	3.0	675	0.3183	0.9
0.0236	4.0	900	0.4110	0.8967
0.027	5.0	1125	0.4898	0.8833
0.0024	6.0	1350	0.5169	0.895
0.0276	7.0	1575	0.7378	0.89
0.0356	8.0	1800	0.5877	0.8983
0.0219	9.0	2025	0.6725	0.8967
0.0365	10.0	2250	0.7831	0.8833
0.0245	11.0	2475	0.6640	0.9033
0.0004	12.0	2700	0.7728	0.88
0.0002	13.0	2925	0.7409	0.8917
0.0001	14.0	3150	0.6940	0.9033
0.0	15.0	3375	0.7164	0.9
0.0	16.0	3600	0.7412	0.9033
0.0	17.0	3825	0.7630	0.9017
0.0001	18.0	4050	0.7681	0.9017
0.0	19.0	4275	0.7425	0.9033
0.0	20.0	4500	0.7631	0.8967
0.0	21.0	4725	0.7304	0.9067
0.0	22.0	4950	0.7313	0.9067
0.0	23.0	5175	0.7463	0.9
0.0	24.0	5400	0.7318	0.9083
0.0047	25.0	5625	0.7438	0.905
0.0	26.0	5850	0.7500	0.905
0.0	27.0	6075	0.7544	0.905
0.0	28.0	6300	0.7484	0.905
0.0046	29.0	6525	0.7585	0.9067
0.0039	30.0	6750	0.7608	0.905
0.0038	31.0	6975	0.7603	0.9033
0.0	32.0	7200	0.7834	0.9067
0.0	33.0	7425	0.7762	0.9033
0.0	34.0	7650	0.7871	0.9033
0.0	35.0	7875	0.7831	0.9067
0.0	36.0	8100	0.7821	0.9017
0.0	37.0	8325	0.7857	0.9067
0.0	38.0	8550	0.7857	0.9017
0.0	39.0	8775	0.7870	0.9033
0.0	40.0	9000	0.7883	0.9033
0.0	41.0	9225	0.7951	0.905
0.0	42.0	9450	0.7962	0.9033
0.0	43.0	9675	0.7972	0.9033
0.0	44.0	9900	0.7957	0.9017
0.0	45.0	10125	0.7978	0.9033
0.0	46.0	10350	0.8000	0.905
0.0025	47.0	10575	0.7996	0.905
0.0	48.0	10800	0.8016	0.905
0.0	49.0	11025	0.8022	0.905
0.0	50.0	11250	0.8015	0.905

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2