square_run_with_16_batch_size

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: 1.4457
• F1 Macro: 0.4685
• F1 Micro: 0.5455
• F1 Weighted: 0.5242
• Precision Macro: 0.5341
• Precision Micro: 0.5455
• Precision Weighted: 0.5870
• Recall Macro: 0.4829
• Recall Micro: 0.5455
• Recall Weighted: 0.5455
• Accuracy: 0.5455

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: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 35

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
1.9976	1.0	29	1.9107	0.0915	0.2045	0.1209	0.0793	0.2045	0.1019	0.1535	0.2045	0.2045	0.2045
1.7575	2.0	58	1.8877	0.1474	0.2348	0.1805	0.1704	0.2348	0.2163	0.1989	0.2348	0.2348	0.2348
1.8336	3.0	87	1.7319	0.1659	0.3182	0.2117	0.1611	0.3182	0.2133	0.2586	0.3182	0.3182	0.3182
1.452	4.0	116	1.5316	0.3336	0.4167	0.3752	0.3518	0.4167	0.3903	0.3682	0.4167	0.4167	0.4167
1.2545	5.0	145	1.4192	0.3999	0.4848	0.4447	0.4601	0.4848	0.5021	0.4318	0.4848	0.4848	0.4848
1.6479	6.0	174	1.3642	0.4649	0.5455	0.5265	0.5072	0.5455	0.5559	0.4750	0.5455	0.5455	0.5455
1.301	7.0	203	1.3015	0.4178	0.5303	0.4735	0.4090	0.5303	0.4503	0.4535	0.5303	0.5303	0.5303
0.9006	8.0	232	1.4861	0.4234	0.4924	0.4699	0.4586	0.4924	0.5286	0.4621	0.4924	0.4924	0.4924
0.4134	9.0	261	1.2101	0.4852	0.5833	0.5545	0.5427	0.5833	0.5894	0.5010	0.5833	0.5833	0.5833
0.9532	10.0	290	1.3783	0.4577	0.5682	0.5204	0.4557	0.5682	0.5160	0.4972	0.5682	0.5682	0.5682
0.4521	11.0	319	1.3602	0.5266	0.6136	0.5923	0.5296	0.6136	0.5907	0.5403	0.6136	0.6136	0.6136
0.633	12.0	348	1.4293	0.5032	0.5833	0.5727	0.4969	0.5833	0.5674	0.5140	0.5833	0.5833	0.5833
0.4268	13.0	377	1.4388	0.5031	0.5833	0.5676	0.5543	0.5833	0.6189	0.5124	0.5833	0.5833	0.5833
0.2857	14.0	406	1.6012	0.5071	0.5833	0.5676	0.5209	0.5833	0.5879	0.5211	0.5833	0.5833	0.5833
0.2606	15.0	435	1.5817	0.5579	0.6136	0.6109	0.5657	0.6136	0.6178	0.5590	0.6136	0.6136	0.6136
0.2028	16.0	464	1.8048	0.4526	0.5227	0.5112	0.4703	0.5227	0.5378	0.4668	0.5227	0.5227	0.5227
0.3251	17.0	493	1.6340	0.4942	0.5833	0.5625	0.5049	0.5833	0.5631	0.5031	0.5833	0.5833	0.5833
0.0369	18.0	522	1.5847	0.5860	0.6439	0.6349	0.6267	0.6439	0.6476	0.5824	0.6439	0.6439	0.6439
0.1133	19.0	551	1.5825	0.5457	0.6288	0.6157	0.5377	0.6288	0.6111	0.5615	0.6288	0.6288	0.6288
0.0457	20.0	580	1.7253	0.5258	0.6136	0.5938	0.5229	0.6136	0.5854	0.5391	0.6136	0.6136	0.6136
0.1109	21.0	609	1.7898	0.5708	0.6212	0.6154	0.6150	0.6212	0.6283	0.5613	0.6212	0.6212	0.6212
0.046	22.0	638	1.7368	0.5656	0.6136	0.6029	0.6021	0.6136	0.6103	0.5615	0.6136	0.6136	0.6136
0.0553	23.0	667	2.2478	0.4822	0.5682	0.5430	0.4851	0.5682	0.5380	0.4975	0.5682	0.5682	0.5682
0.0047	24.0	696	2.1705	0.5133	0.5909	0.5750	0.5158	0.5909	0.5716	0.5220	0.5909	0.5909	0.5909
0.0104	25.0	725	2.2669	0.4950	0.5833	0.5622	0.5035	0.5833	0.5609	0.5038	0.5833	0.5833	0.5833
0.0287	26.0	754	2.0390	0.5267	0.6061	0.5935	0.5265	0.6061	0.5898	0.5346	0.6061	0.6061	0.6061
0.0212	27.0	783	2.1345	0.5344	0.6136	0.6005	0.5308	0.6136	0.5946	0.5449	0.6136	0.6136	0.6136
0.0221	28.0	812	2.1555	0.5607	0.6136	0.6035	0.5953	0.6136	0.6107	0.5583	0.6136	0.6136	0.6136
0.001	29.0	841	2.1102	0.5833	0.6364	0.6289	0.6172	0.6364	0.6353	0.5789	0.6364	0.6364	0.6364
0.0045	30.0	870	2.0669	0.5862	0.6364	0.6290	0.6164	0.6364	0.6326	0.5831	0.6364	0.6364	0.6364
0.0021	31.0	899	2.1442	0.5833	0.6364	0.6282	0.6165	0.6364	0.6330	0.5789	0.6364	0.6364	0.6364
0.0014	32.0	928	2.1435	0.5616	0.6136	0.6049	0.5957	0.6136	0.6099	0.5569	0.6136	0.6136	0.6136
0.0016	33.0	957	2.1279	0.5621	0.6136	0.6047	0.5966	0.6136	0.6093	0.5569	0.6136	0.6136	0.6136
0.0008	34.0	986	2.1310	0.5691	0.6212	0.6127	0.6030	0.6212	0.6170	0.5641	0.6212	0.6212	0.6212
0.0006	35.0	1015	2.1338	0.5690	0.6212	0.6130	0.6026	0.6212	0.6176	0.5641	0.6212	0.6212	0.6212

Framework versions

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