hushem_40x_deit_tiny_sgd_0001_fold2

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: 1.2757
• Accuracy: 0.4667

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
1.3997	1.0	215	1.4647	0.2444
1.3365	2.0	430	1.4330	0.2222
1.2917	3.0	645	1.4094	0.2444
1.2427	4.0	860	1.3927	0.2667
1.2441	5.0	1075	1.3794	0.2667
1.1872	6.0	1290	1.3684	0.2667
1.1795	7.0	1505	1.3589	0.3111
1.1209	8.0	1720	1.3504	0.3333
1.1403	9.0	1935	1.3427	0.3778
1.0825	10.0	2150	1.3360	0.3778
1.0205	11.0	2365	1.3306	0.3778
1.0287	12.0	2580	1.3256	0.4222
1.0526	13.0	2795	1.3201	0.4444
0.979	14.0	3010	1.3158	0.4444
1.009	15.0	3225	1.3119	0.4444
1.0242	16.0	3440	1.3068	0.4444
0.9586	17.0	3655	1.3041	0.4222
0.9705	18.0	3870	1.3009	0.4222
0.9559	19.0	4085	1.2993	0.4222
0.95	20.0	4300	1.2983	0.4444
0.9501	21.0	4515	1.2955	0.4444
0.9287	22.0	4730	1.2949	0.4444
0.8978	23.0	4945	1.2936	0.4444
0.8221	24.0	5160	1.2913	0.4444
0.8642	25.0	5375	1.2902	0.4444
0.8893	26.0	5590	1.2888	0.4444
0.8888	27.0	5805	1.2875	0.4444
0.8399	28.0	6020	1.2872	0.4444
0.8384	29.0	6235	1.2862	0.4444
0.8557	30.0	6450	1.2852	0.4444
0.8264	31.0	6665	1.2846	0.4444
0.7947	32.0	6880	1.2839	0.4222
0.7889	33.0	7095	1.2827	0.4222
0.829	34.0	7310	1.2822	0.4444
0.754	35.0	7525	1.2813	0.4444
0.7758	36.0	7740	1.2807	0.4444
0.8928	37.0	7955	1.2794	0.4444
0.734	38.0	8170	1.2794	0.4444
0.7594	39.0	8385	1.2785	0.4444
0.775	40.0	8600	1.2779	0.4444
0.7835	41.0	8815	1.2773	0.4667
0.7569	42.0	9030	1.2769	0.4667
0.7974	43.0	9245	1.2769	0.4667
0.7959	44.0	9460	1.2766	0.4667
0.8113	45.0	9675	1.2762	0.4667
0.7344	46.0	9890	1.2759	0.4667
0.7955	47.0	10105	1.2758	0.4667
0.7831	48.0	10320	1.2757	0.4667
0.7467	49.0	10535	1.2757	0.4667
0.8192	50.0	10750	1.2757	0.4667

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2