smids_3x_deit_tiny_adamax_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: 1.0425
• Accuracy: 0.8769

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
0.3075	1.0	225	0.3475	0.8502
0.1874	2.0	450	0.4269	0.8519
0.1627	3.0	675	0.3974	0.8652
0.1048	4.0	900	0.4256	0.8669
0.0866	5.0	1125	0.5024	0.8686
0.0482	6.0	1350	0.5673	0.8752
0.0764	7.0	1575	0.8107	0.8652
0.0186	8.0	1800	0.7251	0.8885
0.0334	9.0	2025	0.7854	0.8686
0.0009	10.0	2250	0.8924	0.8619
0.0392	11.0	2475	0.8255	0.8686
0.0005	12.0	2700	0.9410	0.8686
0.0101	13.0	2925	0.9076	0.8702
0.0133	14.0	3150	1.0016	0.8602
0.0	15.0	3375	0.8996	0.8752
0.0001	16.0	3600	0.9808	0.8636
0.0141	17.0	3825	1.0333	0.8586
0.0001	18.0	4050	1.0084	0.8686
0.0101	19.0	4275	0.9333	0.8769
0.0	20.0	4500	0.9573	0.8752
0.0	21.0	4725	0.9496	0.8819
0.0	22.0	4950	0.9897	0.8785
0.0057	23.0	5175	1.0229	0.8719
0.0	24.0	5400	0.9747	0.8785
0.0089	25.0	5625	1.1564	0.8602
0.0065	26.0	5850	1.0567	0.8735
0.0	27.0	6075	1.0448	0.8686
0.0	28.0	6300	1.0218	0.8769
0.0	29.0	6525	1.0556	0.8735
0.007	30.0	6750	0.9967	0.8719
0.0	31.0	6975	1.0179	0.8769
0.0	32.0	7200	1.0202	0.8752
0.0	33.0	7425	1.0274	0.8735
0.0	34.0	7650	1.0303	0.8669
0.0	35.0	7875	1.0234	0.8686
0.0038	36.0	8100	1.0313	0.8702
0.0	37.0	8325	1.0205	0.8735
0.0	38.0	8550	1.0300	0.8702
0.0041	39.0	8775	1.0336	0.8752
0.0	40.0	9000	1.0365	0.8735
0.0	41.0	9225	1.0406	0.8735
0.0	42.0	9450	1.0330	0.8752
0.0	43.0	9675	1.0322	0.8752
0.0	44.0	9900	1.0369	0.8752
0.0	45.0	10125	1.0431	0.8769
0.0	46.0	10350	1.0388	0.8752
0.0	47.0	10575	1.0369	0.8785
0.0	48.0	10800	1.0413	0.8769
0.0025	49.0	11025	1.0423	0.8769
0.0025	50.0	11250	1.0425	0.8769

Framework versions

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