square_run_32_batch

This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.6241
• F1 Macro: 0.5019
• F1 Micro: 0.5758
• F1 Weighted: 0.5679
• Precision Macro: 0.5021
• Precision Micro: 0.5758
• Precision Weighted: 0.5657
• Recall Macro: 0.5073
• Recall Micro: 0.5758
• Recall Weighted: 0.5758
• Accuracy: 0.5758

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: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	F1 Weighted	Precision Macro	Precision Micro	Precision Weighted	Recall Macro	Recall Micro	Recall Weighted	Accuracy
1.9373	1.0	15	1.8818	0.0464	0.1894	0.0615	0.0277	0.1894	0.0367	0.1429	0.1894	0.1894	0.1894
1.869	2.0	30	1.8642	0.1100	0.2652	0.1418	0.075	0.2652	0.0968	0.2063	0.2652	0.2652	0.2652
1.9218	3.0	45	1.8754	0.1163	0.2576	0.1460	0.1316	0.2576	0.1566	0.1905	0.2576	0.2576	0.2576
1.6733	4.0	60	1.6881	0.2445	0.3864	0.3053	0.2427	0.3864	0.2917	0.2992	0.3864	0.3864	0.3864
1.54	5.0	75	1.5528	0.3252	0.4242	0.3856	0.3429	0.4242	0.4101	0.3570	0.4242	0.4242	0.4242
1.4418	6.0	90	1.5737	0.2858	0.3864	0.3213	0.2846	0.3864	0.3243	0.3398	0.3864	0.3864	0.3864
0.8592	7.0	105	1.5408	0.3444	0.4394	0.3965	0.3208	0.4394	0.3674	0.3791	0.4394	0.4394	0.4394
1.1427	8.0	120	1.2804	0.4638	0.5606	0.5317	0.4698	0.5606	0.5280	0.4831	0.5606	0.5606	0.5606
0.7849	9.0	135	1.2880	0.4649	0.5530	0.5291	0.4804	0.5530	0.5401	0.4823	0.5530	0.5530	0.5530
0.6846	10.0	150	1.3130	0.4298	0.5152	0.4811	0.4404	0.5152	0.5005	0.4671	0.5152	0.5152	0.5152
0.4006	11.0	165	1.2958	0.4931	0.5833	0.5598	0.4983	0.5833	0.5756	0.5229	0.5833	0.5833	0.5833
0.4329	12.0	180	1.2990	0.5062	0.5530	0.5562	0.5315	0.5530	0.5874	0.5133	0.5530	0.5530	0.5530
0.482	13.0	195	1.3831	0.4842	0.5152	0.5233	0.5517	0.5152	0.5803	0.4839	0.5152	0.5152	0.5152
0.6409	14.0	210	1.4066	0.5081	0.5985	0.5765	0.5194	0.5985	0.5820	0.5232	0.5985	0.5985	0.5985
0.3206	15.0	225	1.3690	0.5155	0.5606	0.5520	0.6158	0.5606	0.5890	0.5170	0.5606	0.5606	0.5606
0.1773	16.0	240	1.2568	0.5920	0.6515	0.6408	0.6894	0.6515	0.6623	0.5843	0.6515	0.6515	0.6515
0.3259	17.0	255	1.3406	0.5467	0.6061	0.5961	0.5615	0.6061	0.6033	0.5467	0.6061	0.6061	0.6061
0.1123	18.0	270	1.3767	0.5868	0.6364	0.6306	0.6258	0.6364	0.6413	0.5785	0.6364	0.6364	0.6364
0.1129	19.0	285	1.4680	0.5879	0.6439	0.6306	0.6809	0.6439	0.6933	0.5806	0.6439	0.6439	0.6439
0.0651	20.0	300	1.4981	0.6655	0.6894	0.6876	0.7115	0.6894	0.7224	0.6511	0.6894	0.6894	0.6894
0.0685	21.0	315	1.4621	0.6091	0.6515	0.6494	0.6303	0.6515	0.6641	0.6040	0.6515	0.6515	0.6515
0.1469	22.0	330	1.5347	0.5330	0.6212	0.6040	0.5477	0.6212	0.6149	0.5440	0.6212	0.6212	0.6212
0.0289	23.0	345	1.5417	0.5466	0.6288	0.6180	0.5409	0.6288	0.6108	0.5549	0.6288	0.6288	0.6288
0.01	24.0	360	1.5670	0.5475	0.6364	0.6187	0.5435	0.6364	0.6104	0.5594	0.6364	0.6364	0.6364
0.035	25.0	375	1.6037	0.5529	0.6364	0.6209	0.5470	0.6364	0.6156	0.5679	0.6364	0.6364	0.6364
0.0109	26.0	390	1.6752	0.5897	0.6212	0.6203	0.6145	0.6212	0.6527	0.6000	0.6212	0.6212	0.6212
0.038	27.0	405	1.6724	0.5344	0.6136	0.6008	0.5332	0.6136	0.6005	0.5468	0.6136	0.6136	0.6136
0.0116	28.0	420	1.6252	0.5384	0.6212	0.6090	0.5337	0.6212	0.6033	0.5491	0.6212	0.6212	0.6212
0.006	29.0	435	1.5980	0.5572	0.6364	0.6294	0.5529	0.6364	0.6246	0.5634	0.6364	0.6364	0.6364
0.0046	30.0	450	1.5939	0.5605	0.6439	0.6342	0.5546	0.6439	0.6269	0.5687	0.6439	0.6439	0.6439

Framework versions

• Transformers 4.48.2
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0