resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd

bdpc/resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd is a image classification model from bdpc. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: resnet
Access: Public
Created: Nov 6, 2023
Updated: Nov 7, 2023
SHA: 597e7592

Base models

microsoft/resnet-50

Model card

From the Hugging Face model README

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3748
Accuracy: 0.8023
Brier Loss: 0.2845
Nll: 1.8818
F1 Micro: 0.8023
F1 Macro: 0.8020
Ece: 0.0375
Aurc: 0.0534

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: 64
eval_batch_size: 64
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	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	250	0.8880	0.1955	0.8872	5.3865	0.1955	0.1551	0.0582	0.7111
0.9199	2.0	500	0.6464	0.407	0.7284	5.2363	0.4070	0.3745	0.0770	0.4284
0.9199	3.0	750	0.5608	0.5945	0.5337	3.5976	0.5945	0.5912	0.0561	0.1950
0.563	4.0	1000	0.4962	0.6905	0.4235	2.6948	0.6905	0.6885	0.0474	0.1170
0.563	5.0	1250	0.4613	0.7177	0.3858	2.5472	0.7178	0.7181	0.0512	0.0964
0.4567	6.0	1500	0.4372	0.742	0.3584	2.3396	0.7420	0.7425	0.0527	0.0824
0.4567	7.0	1750	0.4271	0.7595	0.3406	2.2123	0.7595	0.7596	0.0459	0.0756
0.4103	8.0	2000	0.4129	0.7658	0.3308	2.1667	0.7658	0.7666	0.0439	0.0704
0.4103	9.0	2250	0.4070	0.7678	0.3296	2.1663	0.7678	0.7692	0.0485	0.0699
0.3836	10.0	2500	0.4017	0.7725	0.3209	2.1207	0.7725	0.7732	0.0426	0.0667
0.3836	11.0	2750	0.3984	0.7768	0.3153	2.0353	0.7768	0.7771	0.0454	0.0651
0.3645	12.0	3000	0.3961	0.7752	0.3124	2.0755	0.7752	0.7754	0.0428	0.0642
0.3645	13.0	3250	0.3961	0.786	0.3071	1.9949	0.786	0.7861	0.0407	0.0612
0.3497	14.0	3500	0.3899	0.7823	0.3053	1.9769	0.7823	0.7823	0.0435	0.0606
0.3497	15.0	3750	0.3873	0.7853	0.3021	1.9881	0.7853	0.7849	0.0479	0.0594
0.3378	16.0	4000	0.3861	0.7833	0.3026	1.9263	0.7833	0.7834	0.0431	0.0593
0.3378	17.0	4250	0.3853	0.7913	0.2970	1.9108	0.7913	0.7917	0.0390	0.0571
0.3271	18.0	4500	0.3840	0.7903	0.2978	1.9643	0.7903	0.7902	0.0377	0.0576
0.3271	19.0	4750	0.3828	0.7915	0.2967	1.9332	0.7915	0.7914	0.0393	0.0572
0.3186	20.0	5000	0.3806	0.7913	0.2938	1.9410	0.7913	0.7909	0.0410	0.0563
0.3186	21.0	5250	0.3815	0.7953	0.2921	1.9285	0.7953	0.7949	0.0387	0.0566
0.3111	22.0	5500	0.3838	0.7895	0.2949	1.9126	0.7895	0.7894	0.0382	0.0570
0.3111	23.0	5750	0.3799	0.7955	0.2902	1.9332	0.7955	0.7955	0.0373	0.0558
0.305	24.0	6000	0.3796	0.7947	0.2912	1.8615	0.7947	0.7940	0.0418	0.0561
0.305	25.0	6250	0.3805	0.7947	0.2912	1.8999	0.7947	0.7940	0.0413	0.0558
0.2993	26.0	6500	0.3842	0.7925	0.2913	1.9451	0.7925	0.7927	0.0339	0.0559
0.2993	27.0	6750	0.3784	0.794	0.2908	1.9151	0.7940	0.7942	0.0389	0.0553
0.2943	28.0	7000	0.3779	0.7957	0.2895	1.8758	0.7957	0.7957	0.0392	0.0549
0.2943	29.0	7250	0.3776	0.7955	0.2892	1.8785	0.7955	0.7947	0.0445	0.0549
0.2905	30.0	7500	0.3775	0.7973	0.2879	1.8786	0.7973	0.7972	0.0379	0.0550
0.2905	31.0	7750	0.3773	0.7945	0.2903	1.9039	0.7945	0.7942	0.0405	0.0551
0.2863	32.0	8000	0.3764	0.7963	0.2880	1.8569	0.7963	0.7962	0.0375	0.0549
0.2863	33.0	8250	0.3775	0.7925	0.2884	1.9070	0.7925	0.7917	0.0411	0.0544
0.2831	34.0	8500	0.3762	0.7935	0.2873	1.8608	0.7935	0.7933	0.0389	0.0547
0.2831	35.0	8750	0.3765	0.7973	0.2868	1.9316	0.7973	0.7970	0.0385	0.0540
0.28	36.0	9000	0.3750	0.7967	0.2857	1.8871	0.7967	0.7965	0.0375	0.0540
0.28	37.0	9250	0.3761	0.793	0.2874	1.8977	0.793	0.7926	0.0405	0.0543
0.2775	38.0	9500	0.3760	0.7983	0.2861	1.8613	0.7983	0.7987	0.0422	0.0540
0.2775	39.0	9750	0.3761	0.7955	0.2870	1.8744	0.7955	0.7957	0.0412	0.0545
0.2755	40.0	10000	0.3753	0.8007	0.2852	1.8640	0.8007	0.8006	0.0345	0.0532
0.2755	41.0	10250	0.3753	0.8023	0.2857	1.8637	0.8023	0.8025	0.0363	0.0535
0.2735	42.0	10500	0.3751	0.7995	0.2851	1.9134	0.7995	0.7994	0.0403	0.0531
0.2735	43.0	10750	0.3753	0.8	0.2857	1.8832	0.8000	0.7996	0.0406	0.0538
0.2717	44.0	11000	0.3746	0.7985	0.2851	1.8545	0.7985	0.7982	0.0432	0.0532
0.2717	45.0	11250	0.3747	0.7985	0.2847	1.8730	0.7985	0.7984	0.0400	0.0534
0.2701	46.0	11500	0.3744	0.801	0.2843	1.8783	0.801	0.8007	0.0411	0.0532
0.2701	47.0	11750	0.3744	0.798	0.2852	1.8843	0.798	0.7975	0.0420	0.0535
0.2694	48.0	12000	0.3753	0.7993	0.2857	1.8875	0.7993	0.7988	0.0405	0.0532
0.2694	49.0	12250	0.3758	0.7965	0.2868	1.8927	0.7965	0.7964	0.0415	0.0539
0.2684	50.0	12500	0.3748	0.8023	0.2845	1.8818	0.8023	0.8020	0.0375	0.0534

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: resnet
License: apache-2.0
Created: Nov 6, 2023
Updated: Nov 7, 2023