resnet-50

corranm/resnet-50

resnet-50 is a image classification model from corranm. Use it when you need a label for an image. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
Model type: resnet
Access: Public
Created: Feb 2, 2025
Updated: Feb 2, 2025
SHA: 28035425

Model card

From the Hugging Face model README

resnet-50

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.6576
F1 Macro: 0.2323
F1 Micro: 0.3485
F1 Weighted: 0.2841
Precision Macro: 0.2908
Precision Micro: 0.3485
Precision Weighted: 0.3373
Recall Macro: 0.2776
Recall Micro: 0.3485
Recall Weighted: 0.3485
Accuracy: 0.3485

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: 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
1.9401	1.0	29	1.9376	0.0673	0.1364	0.0898	0.0524	0.1364	0.0693	0.1020	0.1364	0.1364	0.1364
1.9122	2.0	58	1.9165	0.0601	0.2045	0.0852	0.0463	0.2045	0.0648	0.1433	0.2045	0.2045	0.2045
1.9226	3.0	87	1.8974	0.0729	0.2197	0.1023	0.0542	0.2197	0.0754	0.1547	0.2197	0.2197	0.2197
1.8609	4.0	116	1.8879	0.0479	0.1894	0.0686	0.0293	0.1894	0.0419	0.1323	0.1894	0.1894	0.1894
1.8345	5.0	145	1.8808	0.0498	0.2045	0.0713	0.0301	0.2045	0.0431	0.1429	0.2045	0.2045	0.2045
1.8965	6.0	174	1.8803	0.0555	0.1894	0.0787	0.0379	0.1894	0.0534	0.1327	0.1894	0.1894	0.1894
1.8651	7.0	203	1.8732	0.0787	0.2273	0.1100	0.0607	0.2273	0.0840	0.1604	0.2273	0.2273	0.2273
1.8235	8.0	232	1.8693	0.0573	0.1970	0.0813	0.0393	0.1970	0.0554	0.1380	0.1970	0.1970	0.1970
1.7786	9.0	261	1.8613	0.1112	0.25	0.1502	0.2131	0.25	0.2558	0.1808	0.25	0.25	0.25
1.9601	10.0	290	1.8535	0.1144	0.2576	0.1549	0.1437	0.2576	0.1793	0.1865	0.2576	0.2576	0.2576
1.7922	11.0	319	1.8492	0.1222	0.2727	0.1652	0.1487	0.2727	0.1860	0.1983	0.2727	0.2727	0.2727
1.8398	12.0	348	1.8497	0.1371	0.2727	0.1810	0.1368	0.2727	0.1724	0.2022	0.2727	0.2727	0.2727
1.8811	13.0	377	1.8354	0.1099	0.2424	0.1484	0.1170	0.2424	0.1490	0.1780	0.2424	0.2424	0.2424
1.7813	14.0	406	1.8299	0.1445	0.2955	0.1925	0.1274	0.2955	0.1643	0.2164	0.2955	0.2955	0.2955
1.8719	15.0	435	1.8213	0.1608	0.2955	0.2083	0.1462	0.2955	0.1838	0.2213	0.2955	0.2955	0.2955
1.7755	16.0	464	1.8057	0.1735	0.3182	0.2247	0.1522	0.3182	0.1921	0.2392	0.3182	0.3182	0.3182
1.7729	17.0	493	1.7964	0.1625	0.3106	0.2129	0.1450	0.3106	0.1843	0.2313	0.3106	0.3106	0.3106
1.687	18.0	522	1.7865	0.1719	0.3182	0.2237	0.1576	0.3182	0.1987	0.2381	0.3182	0.3182	0.3182
1.7207	19.0	551	1.7771	0.1823	0.3485	0.2394	0.1572	0.3485	0.2012	0.2592	0.3485	0.3485	0.3485
1.7066	20.0	580	1.7672	0.1857	0.3485	0.2424	0.1578	0.3485	0.2015	0.2607	0.3485	0.3485	0.3485
1.7726	21.0	609	1.7596	0.2147	0.3636	0.2710	0.2530	0.3636	0.2931	0.2766	0.3636	0.3636	0.3636
1.7349	22.0	638	1.7517	0.2081	0.3485	0.2627	0.2145	0.3485	0.2554	0.2660	0.3485	0.3485	0.3485
1.7956	23.0	667	1.7437	0.2018	0.3561	0.2590	0.1970	0.3561	0.2402	0.2687	0.3561	0.3561	0.3561
1.4672	24.0	696	1.7264	0.2033	0.3636	0.2611	0.2975	0.3636	0.3356	0.2740	0.3636	0.3636	0.3636
1.6008	25.0	725	1.7233	0.2323	0.3788	0.2905	0.2533	0.3788	0.2963	0.2898	0.3788	0.3788	0.3788
1.6899	26.0	754	1.7199	0.2261	0.3788	0.2852	0.2426	0.3788	0.2863	0.2887	0.3788	0.3788	0.3788
1.7073	27.0	783	1.7113	0.2171	0.3712	0.2752	0.2305	0.3712	0.2729	0.2819	0.3712	0.3712	0.3712
1.6558	28.0	812	1.6996	0.2311	0.3864	0.2923	0.2212	0.3864	0.2677	0.2955	0.3864	0.3864	0.3864
1.4732	29.0	841	1.7078	0.2320	0.3788	0.2901	0.2301	0.3788	0.2742	0.2909	0.3788	0.3788	0.3788
1.6134	30.0	870	1.7132	0.2248	0.3788	0.2845	0.2245	0.3788	0.2692	0.2887	0.3788	0.3788	0.3788

Framework versions

Transformers 4.48.2
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Task: Image Classification
Library: transformers
Type: resnet
Created: Feb 2, 2025
Updated: Feb 2, 2025