14-classifier-finetuned-padchest

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.9826
• F1: 0.6620

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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	F1
2.0848	1.0	18	2.0904	0.0451
2.0822	2.0	36	2.0842	0.0848
2.0681	3.0	54	2.0742	0.1092
2.0568	4.0	72	2.0624	0.1443
2.0363	5.0	90	2.0420	0.1747
2.0235	6.0	108	2.0288	0.1533
1.9984	7.0	126	2.0008	0.1816
1.9647	8.0	144	1.9752	0.1849
1.9257	9.0	162	1.9481	0.2075
1.8791	10.0	180	1.9060	0.2219
1.8561	11.0	198	1.8678	0.2734
1.803	12.0	216	1.8322	0.2039
1.7461	13.0	234	1.8045	0.2939
1.7169	14.0	252	1.8216	0.3012
1.6773	15.0	270	1.7588	0.3166
1.6724	16.0	288	1.7379	0.2726
1.6286	17.0	306	1.7109	0.3248
1.5533	18.0	324	1.6492	0.3294
1.5075	19.0	342	1.5951	0.3394
1.4789	20.0	360	1.5657	0.3643
1.4077	21.0	378	1.5287	0.3665
1.4146	22.0	396	1.4897	0.4099
1.3583	23.0	414	1.4704	0.3765
1.3486	24.0	432	1.4469	0.3813
1.2947	25.0	450	1.4228	0.4049
1.3272	26.0	468	1.4035	0.4203
1.3048	27.0	486	1.3907	0.4316
1.2898	28.0	504	1.3992	0.4520
1.2204	29.0	522	1.3751	0.4952
1.2298	30.0	540	1.3658	0.4771
1.2036	31.0	558	1.3464	0.4723
1.2314	32.0	576	1.3276	0.5061
1.2201	33.0	594	1.3068	0.5027
1.1737	34.0	612	1.2978	0.5161
1.2102	35.0	630	1.2962	0.4961
1.156	36.0	648	1.2793	0.5172
1.1707	37.0	666	1.2715	0.5125
1.149	38.0	684	1.2728	0.4986
1.1685	39.0	702	1.2525	0.5101
1.1212	40.0	720	1.2446	0.5100
1.095	41.0	738	1.2365	0.5119
1.1166	42.0	756	1.2241	0.5294
1.0775	43.0	774	1.2175	0.5234
1.0768	44.0	792	1.2041	0.5165
1.0395	45.0	810	1.1995	0.5284
1.0857	46.0	828	1.2031	0.5316
1.0447	47.0	846	1.1954	0.5096
1.0504	48.0	864	1.1708	0.5349
1.0229	49.0	882	1.1656	0.5468
1.0715	50.0	900	1.1625	0.5505
1.0401	51.0	918	1.1619	0.5458
1.0477	52.0	936	1.1373	0.5608
1.009	53.0	954	1.1425	0.5740
1.0078	54.0	972	1.1397	0.5622
0.9709	55.0	990	1.1503	0.5813
0.9989	56.0	1008	1.1271	0.5761
0.9704	57.0	1026	1.1332	0.5691
0.9537	58.0	1044	1.1113	0.5910
0.9722	59.0	1062	1.1047	0.5832
0.9889	60.0	1080	1.1005	0.5815
0.9682	61.0	1098	1.0862	0.6137
0.9609	62.0	1116	1.0737	0.6148
0.9688	63.0	1134	1.0580	0.6238
0.9488	64.0	1152	1.0645	0.6253
0.926	65.0	1170	1.0576	0.6188
0.9689	66.0	1188	1.0438	0.6210
0.9445	67.0	1206	1.0409	0.6319
0.938	68.0	1224	1.0302	0.6397
0.9134	69.0	1242	1.0346	0.6337
0.9125	70.0	1260	1.0221	0.6575
0.8879	71.0	1278	1.0146	0.6633
0.9212	72.0	1296	1.0206	0.6384
0.9259	73.0	1314	1.0255	0.6213
0.9224	74.0	1332	1.0190	0.6417
0.9249	75.0	1350	1.0063	0.6371
0.8888	76.0	1368	0.9951	0.6458
0.8799	77.0	1386	1.0045	0.6436
0.9186	78.0	1404	0.9871	0.6449
0.9087	79.0	1422	1.0031	0.6611
0.914	80.0	1440	0.9893	0.6501
0.9012	81.0	1458	0.9876	0.6441
0.8748	82.0	1476	0.9873	0.6533
0.8736	83.0	1494	0.9951	0.6524
0.892	84.0	1512	1.0012	0.6563
0.8746	85.0	1530	0.9944	0.6684
0.8769	86.0	1548	0.9841	0.6558
0.8816	87.0	1566	0.9930	0.6551
0.8889	88.0	1584	0.9880	0.6497
0.8705	89.0	1602	0.9874	0.6564
0.8607	90.0	1620	0.9850	0.6471
0.86	91.0	1638	0.9851	0.6572
0.878	92.0	1656	0.9835	0.6553
0.8592	93.0	1674	0.9784	0.6577
0.8699	94.0	1692	0.9783	0.6568
0.8413	95.0	1710	0.9909	0.6519
0.8944	96.0	1728	0.9759	0.6581
0.8404	97.0	1746	0.9834	0.6640
0.8954	98.0	1764	0.9785	0.6582
0.8539	99.0	1782	0.9746	0.6528
0.8732	100.0	1800	0.9826	0.6620

Framework versions

• Transformers 4.28.0.dev0
• Pytorch 2.0.0+cu117
• Datasets 2.19.0
• Tokenizers 0.12.1