smids_1x_deit_small_adamax_0001_fold5

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: 0.6669
• Accuracy: 0.8983

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
0.4029	1.0	75	0.3535	0.845
0.3043	2.0	150	0.3006	0.88
0.16	3.0	225	0.2739	0.8917
0.1191	4.0	300	0.3321	0.8867
0.1124	5.0	375	0.4055	0.885
0.0475	6.0	450	0.4499	0.89
0.0249	7.0	525	0.4528	0.89
0.0478	8.0	600	0.5581	0.8883
0.0013	9.0	675	0.5854	0.8867
0.0299	10.0	750	0.6219	0.8833
0.0306	11.0	825	0.6492	0.8833
0.0013	12.0	900	0.5930	0.89
0.006	13.0	975	0.6079	0.8933
0.018	14.0	1050	0.6720	0.885
0.009	15.0	1125	0.7215	0.8917
0.0001	16.0	1200	0.5817	0.8967
0.0052	17.0	1275	0.5765	0.8983
0.0097	18.0	1350	0.5975	0.8967
0.0033	19.0	1425	0.6180	0.8983
0.0	20.0	1500	0.6163	0.9
0.0043	21.0	1575	0.5951	0.9
0.0041	22.0	1650	0.6227	0.8983
0.0044	23.0	1725	0.6229	0.895
0.0	24.0	1800	0.6143	0.9
0.0068	25.0	1875	0.6320	0.9017
0.0	26.0	1950	0.6233	0.8983
0.0062	27.0	2025	0.6313	0.8967
0.0083	28.0	2100	0.6337	0.8983
0.0062	29.0	2175	0.6268	0.9033
0.0034	30.0	2250	0.6363	0.9
0.0	31.0	2325	0.6524	0.8983
0.0	32.0	2400	0.6386	0.9
0.0057	33.0	2475	0.6534	0.9
0.0	34.0	2550	0.6542	0.8967
0.0048	35.0	2625	0.6498	0.9017
0.0	36.0	2700	0.6512	0.9033
0.003	37.0	2775	0.6532	0.9017
0.0	38.0	2850	0.6543	0.8967
0.0	39.0	2925	0.6569	0.8967
0.0	40.0	3000	0.6561	0.9
0.0028	41.0	3075	0.6588	0.8967
0.0	42.0	3150	0.6601	0.9017
0.007	43.0	3225	0.6640	0.9017
0.0	44.0	3300	0.6634	0.8967
0.0024	45.0	3375	0.6647	0.9033
0.0	46.0	3450	0.6647	0.8983
0.0051	47.0	3525	0.6655	0.8983
0.0049	48.0	3600	0.6656	0.8983
0.0	49.0	3675	0.6665	0.8967
0.0043	50.0	3750	0.6669	0.8983

Framework versions

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