convnext-tiny-224-finetuned-biopsy

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

• Loss: 0.0688
• Accuracy: 0.9816

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	Accuracy
1.2488	1.0	42	1.1914	0.5360
0.9265	2.0	84	0.8634	0.5544
0.5701	3.0	126	0.4834	0.8543
0.4041	4.0	168	0.2996	0.9213
0.2747	5.0	210	0.2743	0.9146
0.2518	6.0	252	0.1826	0.9497
0.2363	7.0	294	0.1731	0.9497
0.1782	8.0	336	0.1870	0.9363
0.2122	9.0	378	0.1327	0.9615
0.1856	10.0	420	0.2082	0.9313
0.1736	11.0	462	0.1306	0.9564
0.1423	12.0	504	0.0989	0.9732
0.1296	13.0	546	0.0949	0.9732
0.1158	14.0	588	0.1084	0.9631
0.1383	15.0	630	0.0865	0.9715
0.1384	16.0	672	0.0879	0.9715
0.0924	17.0	714	0.0758	0.9782
0.0966	18.0	756	0.0866	0.9682
0.1324	19.0	798	0.0876	0.9715
0.0995	20.0	840	0.0990	0.9648
0.083	21.0	882	0.0911	0.9698
0.082	22.0	924	0.0816	0.9799
0.1038	23.0	966	0.1453	0.9430
0.0751	24.0	1008	0.0877	0.9732
0.0733	25.0	1050	0.0878	0.9682
0.0813	26.0	1092	0.0688	0.9816
0.0788	27.0	1134	0.0732	0.9782
0.0617	28.0	1176	0.0722	0.9749
0.0568	29.0	1218	0.0883	0.9648
0.0701	30.0	1260	0.0703	0.9765
0.0535	31.0	1302	0.0792	0.9782
0.0716	32.0	1344	0.0684	0.9799
0.0419	33.0	1386	0.0666	0.9816
0.054	34.0	1428	0.0768	0.9749
0.0332	35.0	1470	0.0717	0.9799
0.0524	36.0	1512	0.1067	0.9715
0.0372	37.0	1554	0.0604	0.9816
0.0692	38.0	1596	0.0579	0.9799
0.038	39.0	1638	0.0824	0.9732
0.0524	40.0	1680	0.0635	0.9765
0.0429	41.0	1722	0.0644	0.9816
0.0705	42.0	1764	0.0747	0.9765
0.0325	43.0	1806	0.0685	0.9816
0.0446	44.0	1848	0.0683	0.9782
0.0439	45.0	1890	0.0707	0.9749
0.0346	46.0	1932	0.0642	0.9782
0.0504	47.0	1974	0.0654	0.9799
0.0379	48.0	2016	0.0651	0.9765
0.0433	49.0	2058	0.0654	0.9765
0.0337	50.0	2100	0.0655	0.9765

Framework versions

• Transformers 4.44.2
• Pytorch 2.5.0+cu121
• Datasets 3.0.2
• Tokenizers 0.19.1