metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
model-index:
  - name: convnext-tiny-224-finetuned-brs2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7924528301886793
          - name: F1
            type: f1
            value: 0.7555555555555556

convnext-tiny-224-finetuned-brs2

This model is a fine-tuned version of facebook/convnext-tiny-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.2502
Accuracy: 0.7925
F1: 0.7556
Precision (ppv): 0.8095
Recall (sensitivity): 0.7083
Specificity: 0.8621
Npv: 0.7812
Auc: 0.7852

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision (ppv)	Recall (sensitivity)	Specificity	Npv	Auc
0.6884	1.89	100	0.6907	0.5472	0.4286	0.5	0.375	0.6897	0.5714	0.5323
0.5868	3.77	200	0.6604	0.6415	0.4242	0.7778	0.2917	0.9310	0.6136	0.6114
0.4759	5.66	300	0.6273	0.6604	0.5	0.75	0.375	0.8966	0.6341	0.6358
0.3599	7.55	400	0.6520	0.6604	0.5	0.75	0.375	0.8966	0.6341	0.6358
0.3248	9.43	500	0.9115	0.6415	0.4571	0.7273	0.3333	0.8966	0.6190	0.6149
0.3117	11.32	600	0.8608	0.6604	0.5263	0.7143	0.4167	0.8621	0.6410	0.6394
0.4208	13.21	700	0.8774	0.6792	0.5641	0.7333	0.4583	0.8621	0.6579	0.6602
0.5267	15.09	800	1.0131	0.6792	0.5405	0.7692	0.4167	0.8966	0.65	0.6566
0.234	16.98	900	1.1498	0.6981	0.5556	0.8333	0.4167	0.9310	0.6585	0.6739
0.7581	18.87	1000	1.0952	0.7170	0.6154	0.8	0.5	0.8966	0.6842	0.6983
0.1689	20.75	1100	1.1653	0.6981	0.5789	0.7857	0.4583	0.8966	0.6667	0.6774
0.0765	22.64	1200	1.1245	0.7170	0.6667	0.7143	0.625	0.7931	0.7188	0.7091
0.6287	24.53	1300	1.2222	0.6981	0.6	0.75	0.5	0.8621	0.6757	0.6810
0.0527	26.42	1400	1.2350	0.7358	0.6818	0.75	0.625	0.8276	0.7273	0.7263
0.3622	28.3	1500	1.1022	0.7547	0.6667	0.8667	0.5417	0.9310	0.7105	0.7364
0.3227	30.19	1600	1.1541	0.7170	0.6154	0.8	0.5	0.8966	0.6842	0.6983
0.3849	32.08	1700	1.2818	0.7170	0.6154	0.8	0.5	0.8966	0.6842	0.6983
0.4528	33.96	1800	1.3213	0.6981	0.5789	0.7857	0.4583	0.8966	0.6667	0.6774
0.1824	35.85	1900	1.3171	0.7170	0.6512	0.7368	0.5833	0.8276	0.7059	0.7055
0.0367	37.74	2000	1.4484	0.7170	0.6154	0.8	0.5	0.8966	0.6842	0.6983
0.07	39.62	2100	1.3521	0.7547	0.6977	0.7895	0.625	0.8621	0.7353	0.7435
0.0696	41.51	2200	1.2636	0.7358	0.65	0.8125	0.5417	0.8966	0.7027	0.7191
0.1554	43.4	2300	1.2225	0.7358	0.6667	0.7778	0.5833	0.8621	0.7143	0.7227
0.2346	45.28	2400	1.2627	0.7547	0.6829	0.8235	0.5833	0.8966	0.7222	0.7399
0.097	47.17	2500	1.4892	0.7170	0.6667	0.7143	0.625	0.7931	0.7188	0.7091
0.2494	49.06	2600	1.5282	0.7170	0.6512	0.7368	0.5833	0.8276	0.7059	0.7055
0.0734	50.94	2700	1.3989	0.7170	0.6341	0.7647	0.5417	0.8621	0.6944	0.7019
0.1077	52.83	2800	1.5155	0.6792	0.5641	0.7333	0.4583	0.8621	0.6579	0.6602
0.2456	54.72	2900	1.4400	0.7170	0.6512	0.7368	0.5833	0.8276	0.7059	0.7055
0.0823	56.6	3000	1.4511	0.7358	0.65	0.8125	0.5417	0.8966	0.7027	0.7191
0.0471	58.49	3100	1.5114	0.7547	0.6829	0.8235	0.5833	0.8966	0.7222	0.7399
0.0144	60.38	3200	1.4412	0.7925	0.7317	0.8824	0.625	0.9310	0.75	0.7780
0.1235	62.26	3300	1.2029	0.7547	0.6977	0.7895	0.625	0.8621	0.7353	0.7435
0.0121	64.15	3400	1.4925	0.7358	0.6667	0.7778	0.5833	0.8621	0.7143	0.7227
0.2126	66.04	3500	1.3614	0.7547	0.6667	0.8667	0.5417	0.9310	0.7105	0.7364
0.0496	67.92	3600	1.2960	0.7736	0.7143	0.8333	0.625	0.8966	0.7429	0.7608
0.1145	69.81	3700	1.3763	0.7547	0.6829	0.8235	0.5833	0.8966	0.7222	0.7399
0.1272	71.7	3800	1.6328	0.7170	0.5946	0.8462	0.4583	0.9310	0.675	0.6947
0.0007	73.58	3900	1.5622	0.7547	0.6977	0.7895	0.625	0.8621	0.7353	0.7435
0.0101	75.47	4000	1.1811	0.7925	0.7442	0.8421	0.6667	0.8966	0.7647	0.7816
0.0002	77.36	4100	1.8533	0.6981	0.5789	0.7857	0.4583	0.8966	0.6667	0.6774
0.0423	79.25	4200	1.2510	0.7547	0.6977	0.7895	0.625	0.8621	0.7353	0.7435
0.0036	81.13	4300	1.3443	0.7547	0.6829	0.8235	0.5833	0.8966	0.7222	0.7399
0.0432	83.02	4400	1.2864	0.7736	0.7273	0.8	0.6667	0.8621	0.7576	0.7644
0.0021	84.91	4500	0.8999	0.7925	0.7755	0.76	0.7917	0.7931	0.8214	0.7924
0.0002	86.79	4600	1.3634	0.7925	0.7442	0.8421	0.6667	0.8966	0.7647	0.7816
0.0044	88.68	4700	1.7830	0.7358	0.65	0.8125	0.5417	0.8966	0.7027	0.7191
0.0003	90.57	4800	1.2640	0.7736	0.7273	0.8	0.6667	0.8621	0.7576	0.7644
0.0253	92.45	4900	1.2649	0.7925	0.7442	0.8421	0.6667	0.8966	0.7647	0.7816
0.0278	94.34	5000	1.7485	0.7170	0.6512	0.7368	0.5833	0.8276	0.7059	0.7055
0.1608	96.23	5100	1.2641	0.8113	0.7727	0.85	0.7083	0.8966	0.7879	0.8024
0.0017	98.11	5200	1.6380	0.7170	0.6667	0.7143	0.625	0.7931	0.7188	0.7091
0.001	100.0	5300	1.2502	0.7925	0.7556	0.8095	0.7083	0.8621	0.7812	0.7852

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1