smids_1x_deit_tiny_adamax_0001_fold5

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: 0.8318
• Accuracy: 0.8767

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
0.4622	1.0	75	0.3902	0.85
0.3997	2.0	150	0.3259	0.8767
0.1811	3.0	225	0.3681	0.8767
0.2239	4.0	300	0.3692	0.8717
0.1061	5.0	375	0.3361	0.89
0.0643	6.0	450	0.4246	0.8867
0.0349	7.0	525	0.5503	0.865
0.0577	8.0	600	0.4923	0.89
0.0047	9.0	675	0.6463	0.875
0.0074	10.0	750	0.6530	0.8683
0.037	11.0	825	0.6041	0.8767
0.0089	12.0	900	0.6572	0.8833
0.0191	13.0	975	0.7128	0.8867
0.0365	14.0	1050	0.8168	0.8733
0.0272	15.0	1125	0.7575	0.875
0.0001	16.0	1200	0.7279	0.8817
0.0112	17.0	1275	0.7893	0.885
0.0208	18.0	1350	0.8683	0.8667
0.0094	19.0	1425	0.8173	0.875
0.0001	20.0	1500	0.8675	0.8633
0.005	21.0	1575	0.8404	0.8633
0.0089	22.0	1650	0.8394	0.8633
0.0106	23.0	1725	0.7646	0.8733
0.0	24.0	1800	0.7970	0.8667
0.0166	25.0	1875	0.7834	0.8767
0.0	26.0	1950	0.7904	0.8833
0.0062	27.0	2025	0.8106	0.8667
0.0107	28.0	2100	0.7803	0.8767
0.0105	29.0	2175	0.7751	0.875
0.0047	30.0	2250	0.7593	0.875
0.0	31.0	2325	0.8560	0.8733
0.0	32.0	2400	0.7908	0.87
0.0066	33.0	2475	0.8221	0.875
0.0	34.0	2550	0.8079	0.8733
0.0052	35.0	2625	0.8173	0.875
0.0	36.0	2700	0.8258	0.8733
0.0045	37.0	2775	0.8236	0.8733
0.0	38.0	2850	0.8199	0.875
0.0	39.0	2925	0.8205	0.8733
0.0	40.0	3000	0.8239	0.8733
0.0036	41.0	3075	0.8262	0.8767
0.0	42.0	3150	0.8270	0.875
0.0074	43.0	3225	0.8280	0.875
0.0	44.0	3300	0.8301	0.875
0.0026	45.0	3375	0.8317	0.875
0.0	46.0	3450	0.8313	0.8767
0.0057	47.0	3525	0.8305	0.875
0.0054	48.0	3600	0.8318	0.8767
0.0	49.0	3675	0.8319	0.8767
0.0042	50.0	3750	0.8318	0.8767

Framework versions

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