smids_3x_deit_small_sgd_00001_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: 1.0145
• Accuracy: 0.4875

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: 1e-05
• 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
1.0768	1.0	225	1.0706	0.4276
1.0941	2.0	450	1.0680	0.4293
1.0918	3.0	675	1.0655	0.4309
1.0511	4.0	900	1.0630	0.4376
1.0876	5.0	1125	1.0606	0.4393
1.0339	6.0	1350	1.0583	0.4393
1.0616	7.0	1575	1.0561	0.4459
1.0897	8.0	1800	1.0540	0.4459
1.053	9.0	2025	1.0518	0.4493
1.0515	10.0	2250	1.0498	0.4509
1.0879	11.0	2475	1.0479	0.4542
1.0316	12.0	2700	1.0459	0.4542
1.0424	13.0	2925	1.0441	0.4576
1.0786	14.0	3150	1.0424	0.4609
1.061	15.0	3375	1.0407	0.4626
1.064	16.0	3600	1.0390	0.4642
1.0184	17.0	3825	1.0374	0.4626
1.0313	18.0	4050	1.0359	0.4626
1.0429	19.0	4275	1.0344	0.4642
1.0308	20.0	4500	1.0330	0.4642
1.049	21.0	4725	1.0317	0.4659
1.0164	22.0	4950	1.0304	0.4642
1.0457	23.0	5175	1.0292	0.4642
1.0471	24.0	5400	1.0280	0.4626
1.0294	25.0	5625	1.0269	0.4642
1.0309	26.0	5850	1.0258	0.4659
1.0318	27.0	6075	1.0248	0.4659
1.0436	28.0	6300	1.0238	0.4676
1.0288	29.0	6525	1.0229	0.4725
1.0425	30.0	6750	1.0220	0.4742
1.0267	31.0	6975	1.0212	0.4792
1.0174	32.0	7200	1.0204	0.4809
1.0197	33.0	7425	1.0197	0.4809
1.0313	34.0	7650	1.0190	0.4809
1.0296	35.0	7875	1.0184	0.4809
1.0429	36.0	8100	1.0179	0.4842
1.0312	37.0	8325	1.0173	0.4859
1.0214	38.0	8550	1.0169	0.4842
1.0321	39.0	8775	1.0164	0.4859
1.0329	40.0	9000	1.0161	0.4859
1.0094	41.0	9225	1.0157	0.4875
0.9973	42.0	9450	1.0154	0.4875
1.0326	43.0	9675	1.0152	0.4875
1.0086	44.0	9900	1.0150	0.4875
1.0104	45.0	10125	1.0148	0.4875
1.0211	46.0	10350	1.0147	0.4875
0.9952	47.0	10575	1.0146	0.4875
0.9977	48.0	10800	1.0146	0.4875
1.0187	49.0	11025	1.0146	0.4875
1.0188	50.0	11250	1.0145	0.4875

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2