8-classifier-finetuned-padchest

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

• Loss: 0.2276
• F1: 0.9325

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.6321	1.0	18	0.5224	0.7896
0.4633	2.0	36	0.3809	0.7896
0.3552	3.0	54	0.3305	0.7896
0.2718	4.0	72	0.2696	0.8197
0.2345	5.0	90	0.2178	0.9149
0.211	6.0	108	0.2405	0.8861
0.2208	7.0	126	0.2713	0.8605
0.1698	8.0	144	0.1747	0.9422
0.1547	9.0	162	0.1783	0.9322
0.1697	10.0	180	0.1629	0.9350
0.1684	11.0	198	0.1740	0.9319
0.1722	12.0	216	0.1885	0.9173
0.158	13.0	234	0.1637	0.9331
0.1469	14.0	252	0.1716	0.9325
0.1271	15.0	270	0.1700	0.9384
0.131	16.0	288	0.1785	0.9409
0.1245	17.0	306	0.2124	0.9206
0.1182	18.0	324	0.1715	0.9322
0.1082	19.0	342	0.1946	0.9322
0.1274	20.0	360	0.1757	0.9379
0.1115	21.0	378	0.1908	0.9307
0.0995	22.0	396	0.2001	0.9289
0.0996	23.0	414	0.1820	0.9293
0.0993	24.0	432	0.2095	0.9355
0.1006	25.0	450	0.1973	0.9314
0.0703	26.0	468	0.1934	0.9389
0.0901	27.0	486	0.2276	0.9238
0.0827	28.0	504	0.1949	0.936
0.0701	29.0	522	0.2076	0.9317
0.0813	30.0	540	0.2001	0.9374
0.0776	31.0	558	0.2440	0.9357
0.0842	32.0	576	0.2163	0.9271
0.0872	33.0	594	0.2248	0.9332
0.0743	34.0	612	0.2007	0.9344
0.0692	35.0	630	0.1971	0.9283
0.0763	36.0	648	0.2094	0.9393
0.0714	37.0	666	0.2139	0.9271
0.0683	38.0	684	0.2065	0.9331
0.0698	39.0	702	0.2177	0.9295
0.0507	40.0	720	0.2171	0.9344
0.0523	41.0	738	0.2240	0.9344
0.0546	42.0	756	0.2083	0.9394
0.0695	43.0	774	0.2171	0.936
0.0634	44.0	792	0.2193	0.9301
0.0462	45.0	810	0.2017	0.9409
0.0581	46.0	828	0.2209	0.9350
0.0468	47.0	846	0.2335	0.9301
0.0424	48.0	864	0.2294	0.9301
0.0472	49.0	882	0.2310	0.9350
0.044	50.0	900	0.2276	0.9325

Framework versions

• Transformers 4.28.0.dev0
• Pytorch 2.0.0+cu117
• Datasets 2.18.0
• Tokenizers 0.13.3