This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3394
F1 Macro: 0.4627
F1 Micro: 0.5606
F1 Weighted: 0.5294
Precision Macro: 0.4704
Precision Micro: 0.5606
Precision Weighted: 0.5310
Recall Macro: 0.4855
Recall Micro: 0.5606
Recall Weighted: 0.5606
Accuracy: 0.5606

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: 8
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: 45

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
1.903	1.0	29	1.8868	0.0658	0.1742	0.0900	0.0502	0.1742	0.0693	0.1293	0.1742	0.1742	0.1742
1.8662	2.0	58	1.8740	0.0754	0.2197	0.1004	0.0603	0.2197	0.0773	0.1580	0.2197	0.2197	0.2197
1.9291	3.0	87	1.8862	0.0485	0.2045	0.0695	0.0292	0.2045	0.0418	0.1429	0.2045	0.2045	0.2045
1.7838	4.0	116	1.8127	0.1171	0.2652	0.1474	0.1092	0.2652	0.1321	0.1973	0.2652	0.2652	0.2652
1.7113	5.0	145	1.6979	0.2133	0.3485	0.2592	0.3189	0.3485	0.3631	0.2822	0.3485	0.3485	0.3485
1.6459	6.0	174	1.5577	0.2714	0.3939	0.3225	0.4296	0.3939	0.4531	0.3198	0.3939	0.3939	0.3939
1.4829	7.0	203	1.3814	0.4069	0.5227	0.4611	0.3786	0.5227	0.4216	0.4511	0.5227	0.5227	0.5227
1.2847	8.0	232	1.3783	0.3675	0.4545	0.4176	0.4992	0.4545	0.5702	0.4080	0.4545	0.4545	0.4545
0.7746	9.0	261	1.1536	0.4579	0.5758	0.5298	0.5301	0.5758	0.5896	0.4853	0.5758	0.5758	0.5758
1.0172	10.0	290	1.2211	0.4700	0.5909	0.5365	0.5722	0.5909	0.6399	0.5182	0.5909	0.5909	0.5909
0.7865	11.0	319	1.1357	0.5282	0.6136	0.5961	0.5342	0.6136	0.6009	0.5432	0.6136	0.6136	0.6136
0.8335	12.0	348	1.1530	0.5315	0.6061	0.6017	0.5365	0.6061	0.6209	0.5489	0.6061	0.6061	0.6061
0.6959	13.0	377	1.1307	0.5638	0.6667	0.6451	0.5912	0.6667	0.6615	0.5773	0.6667	0.6667	0.6667
0.5864	14.0	406	1.1957	0.5211	0.5985	0.5894	0.5537	0.5985	0.6275	0.5389	0.5985	0.5985	0.5985
0.6145	15.0	435	0.9957	0.6086	0.7045	0.6833	0.6164	0.7045	0.6791	0.6160	0.7045	0.7045	0.7045
0.5632	16.0	464	1.2302	0.5112	0.5985	0.5781	0.5219	0.5985	0.5853	0.5236	0.5985	0.5985	0.5985
0.3392	17.0	493	1.1925	0.5335	0.6288	0.6043	0.5903	0.6288	0.6435	0.5355	0.6288	0.6288	0.6288
0.2998	18.0	522	1.1444	0.5544	0.6364	0.6251	0.5520	0.6364	0.6248	0.5670	0.6364	0.6364	0.6364
0.2706	19.0	551	1.1072	0.5579	0.6439	0.6308	0.5790	0.6439	0.6404	0.5571	0.6439	0.6439	0.6439
0.2012	20.0	580	1.1353	0.5278	0.6212	0.6012	0.5433	0.6212	0.6063	0.5346	0.6212	0.6212	0.6212
0.532	21.0	609	1.2503	0.5421	0.6212	0.6079	0.5651	0.6212	0.6253	0.5488	0.6212	0.6212	0.6212
0.0963	22.0	638	1.2203	0.5702	0.6288	0.6227	0.5807	0.6288	0.6327	0.5745	0.6288	0.6288	0.6288
0.1076	23.0	667	1.3798	0.5216	0.6136	0.5894	0.5339	0.6136	0.5971	0.5370	0.6136	0.6136	0.6136
0.1773	24.0	696	1.3129	0.5422	0.6288	0.6169	0.5581	0.6288	0.6253	0.5453	0.6288	0.6288	0.6288
0.0598	25.0	725	1.2855	0.5633	0.6515	0.6381	0.5846	0.6515	0.6562	0.5713	0.6515	0.6515	0.6515
0.0632	26.0	754	1.3155	0.6414	0.6591	0.6643	0.6525	0.6591	0.6925	0.6585	0.6591	0.6591	0.6591
0.0644	27.0	783	1.3211	0.5588	0.6439	0.6315	0.5745	0.6439	0.6357	0.5595	0.6439	0.6439	0.6439
0.1495	28.0	812	1.4196	0.5539	0.6364	0.6245	0.5650	0.6364	0.6270	0.5556	0.6364	0.6364	0.6364
0.0413	29.0	841	1.4027	0.5378	0.6136	0.6102	0.5405	0.6136	0.6100	0.5380	0.6136	0.6136	0.6136
0.0323	30.0	870	1.4302	0.5641	0.6364	0.6329	0.5689	0.6364	0.6430	0.5712	0.6364	0.6364	0.6364
0.0452	31.0	899	1.4577	0.5706	0.6515	0.6412	0.5835	0.6515	0.6478	0.5738	0.6515	0.6515	0.6515
0.0285	32.0	928	1.4224	0.5597	0.6439	0.6300	0.5618	0.6439	0.6250	0.5657	0.6439	0.6439	0.6439
0.0241	33.0	957	1.4513	0.5542	0.6364	0.6252	0.5700	0.6364	0.6309	0.5533	0.6364	0.6364	0.6364
0.0224	34.0	986	1.4701	0.5795	0.6742	0.6545	0.5856	0.6742	0.6523	0.5902	0.6742	0.6742	0.6742
0.0228	35.0	1015	1.4697	0.5772	0.6591	0.6489	0.5870	0.6591	0.6497	0.5774	0.6591	0.6591	0.6591
0.0231	36.0	1044	1.5315	0.5745	0.6591	0.6491	0.5783	0.6591	0.6483	0.5788	0.6591	0.6591	0.6591
0.0457	37.0	1073	1.5210	0.5532	0.6439	0.6277	0.5641	0.6439	0.6317	0.5606	0.6439	0.6439	0.6439
0.0197	38.0	1102	1.4956	0.5636	0.6515	0.6386	0.5590	0.6515	0.6296	0.5714	0.6515	0.6515	0.6515
0.0219	39.0	1131	1.4910	0.5981	0.6591	0.6540	0.6063	0.6591	0.6554	0.5970	0.6591	0.6591	0.6591
0.0212	40.0	1160	1.5050	0.5912	0.6515	0.6462	0.5997	0.6515	0.6472	0.5898	0.6515	0.6515	0.6515
0.0212	41.0	1189	1.5091	0.5977	0.6591	0.6537	0.6080	0.6591	0.6558	0.5955	0.6591	0.6591	0.6591
0.0202	42.0	1218	1.4961	0.5655	0.6515	0.6411	0.5708	0.6515	0.6411	0.5695	0.6515	0.6515	0.6515
0.0216	43.0	1247	1.4917	0.5655	0.6515	0.6411	0.5708	0.6515	0.6411	0.5695	0.6515	0.6515	0.6515
0.0199	44.0	1276	1.4855	0.5674	0.6515	0.6423	0.5694	0.6515	0.6401	0.5717	0.6515	0.6515	0.6515
0.027	45.0	1305	1.4832	0.5674	0.6515	0.6423	0.5694	0.6515	0.6401	0.5717	0.6515	0.6515	0.6515

Framework versions

Transformers 4.48.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

corranm/squarerun

squarerun

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

corranm/
squarerun