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