smids_3x_deit_small_adamax_001_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: 0.9786
• Accuracy: 0.8933

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: 0.001
• 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
0.4746	1.0	225	0.4469	0.7917
0.3712	2.0	450	0.4363	0.8183
0.2858	3.0	675	0.3457	0.8733
0.1805	4.0	900	0.4133	0.865
0.1699	5.0	1125	0.3336	0.89
0.1967	6.0	1350	0.3958	0.88
0.1372	7.0	1575	0.4702	0.87
0.1698	8.0	1800	0.3428	0.8867
0.1529	9.0	2025	0.5351	0.8583
0.0621	10.0	2250	0.5492	0.8733
0.1311	11.0	2475	0.5821	0.8767
0.1035	12.0	2700	0.6050	0.8667
0.0328	13.0	2925	0.6304	0.875
0.036	14.0	3150	0.5969	0.8983
0.0422	15.0	3375	0.6326	0.8817
0.0088	16.0	3600	0.6452	0.8733
0.0148	17.0	3825	0.5210	0.8883
0.0267	18.0	4050	0.6134	0.88
0.0523	19.0	4275	0.6448	0.8933
0.0182	20.0	4500	0.7298	0.8783
0.0004	21.0	4725	0.7641	0.8683
0.0037	22.0	4950	0.8419	0.87
0.0038	23.0	5175	0.7762	0.8817
0.0002	24.0	5400	0.7488	0.8883
0.0367	25.0	5625	0.6912	0.8767
0.0002	26.0	5850	0.6890	0.895
0.0002	27.0	6075	0.6160	0.9017
0.0001	28.0	6300	0.6922	0.8967
0.007	29.0	6525	0.8317	0.885
0.0083	30.0	6750	0.6909	0.8983
0.0048	31.0	6975	0.7613	0.8967
0.0	32.0	7200	0.8055	0.895
0.0	33.0	7425	0.8267	0.8917
0.0	34.0	7650	0.8303	0.8917
0.0	35.0	7875	0.8741	0.8967
0.0	36.0	8100	0.8381	0.8967
0.0024	37.0	8325	0.8583	0.8983
0.0	38.0	8550	0.9234	0.8917
0.0	39.0	8775	0.8565	0.8967
0.0	40.0	9000	0.8898	0.8917
0.0	41.0	9225	0.9312	0.8917
0.0	42.0	9450	0.9410	0.8933
0.0	43.0	9675	0.9514	0.8933
0.0	44.0	9900	0.9553	0.8933
0.0	45.0	10125	0.9599	0.8933
0.0	46.0	10350	0.9610	0.8933
0.0027	47.0	10575	0.9689	0.8933
0.0	48.0	10800	0.9724	0.8933
0.0	49.0	11025	0.9761	0.8933
0.0	50.0	11250	0.9786	0.8933

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2