smids_1x_deit_small_rms_0001_fold2

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: 0.6765
• Accuracy: 0.7571

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
1.1018	1.0	75	1.0371	0.4260
0.991	2.0	150	0.9921	0.4792
0.9572	3.0	225	0.9534	0.4692
0.9605	4.0	300	0.9410	0.4942
1.0177	5.0	375	0.9782	0.4792
0.8824	6.0	450	0.9530	0.4775
0.9937	7.0	525	1.2068	0.4143
0.9218	8.0	600	0.9562	0.4842
0.9543	9.0	675	0.9220	0.4892
0.9236	10.0	750	0.9222	0.4958
0.9252	11.0	825	0.8952	0.5075
0.8897	12.0	900	0.8977	0.5042
0.8737	13.0	975	0.8116	0.5691
0.8039	14.0	1050	0.7757	0.5790
0.7793	15.0	1125	0.8219	0.5824
0.8231	16.0	1200	0.7679	0.6057
0.8017	17.0	1275	0.7881	0.5874
0.7891	18.0	1350	0.8079	0.5990
0.7545	19.0	1425	0.7312	0.6456
0.7578	20.0	1500	0.7753	0.6123
0.8565	21.0	1575	0.7816	0.6073
0.7262	22.0	1650	0.8273	0.5840
0.7951	23.0	1725	0.7247	0.6339
0.7867	24.0	1800	0.7753	0.6173
0.7108	25.0	1875	0.7213	0.6805
0.6679	26.0	1950	0.7131	0.6556
0.7183	27.0	2025	0.7432	0.6456
0.6589	28.0	2100	0.6919	0.6938
0.6988	29.0	2175	0.7014	0.6689
0.6704	30.0	2250	0.6664	0.7038
0.6348	31.0	2325	0.6647	0.7038
0.6316	32.0	2400	0.6641	0.6988
0.5915	33.0	2475	0.6743	0.6839
0.6102	34.0	2550	0.6568	0.7038
0.5452	35.0	2625	0.6346	0.7271
0.5721	36.0	2700	0.6475	0.7255
0.5908	37.0	2775	0.6240	0.7388
0.6069	38.0	2850	0.6538	0.7354
0.4947	39.0	2925	0.6146	0.7438
0.4469	40.0	3000	0.6694	0.7038
0.5595	41.0	3075	0.5969	0.7438
0.524	42.0	3150	0.6251	0.7438
0.5223	43.0	3225	0.6144	0.7338
0.4414	44.0	3300	0.6374	0.7404
0.5093	45.0	3375	0.6328	0.7488
0.4116	46.0	3450	0.6556	0.7537
0.414	47.0	3525	0.6472	0.7604
0.445	48.0	3600	0.6566	0.7488
0.3661	49.0	3675	0.6775	0.7504
0.3935	50.0	3750	0.6765	0.7571

Framework versions

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