organoids-prova_organoid

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

• Loss: 0.3433
• Accuracy: 0.8576

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: 128
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 512
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2121	0.99	36	1.3066	0.4116
0.8905	1.99	72	0.9344	0.6749
0.6942	2.98	108	0.6875	0.7507
0.6087	4.0	145	0.5493	0.7896
0.5896	4.99	181	0.5028	0.7993
0.6168	5.99	217	0.4787	0.8100
0.5627	6.98	253	0.4373	0.8319
0.5654	8.0	290	0.4324	0.8299
0.5204	8.99	326	0.4130	0.8319
0.5581	9.99	362	0.4264	0.8241
0.5232	10.98	398	0.4074	0.8294
0.483	12.0	435	0.3850	0.8445
0.5208	12.99	471	0.3791	0.8489
0.4937	13.99	507	0.3723	0.8528
0.4436	14.98	543	0.3910	0.8440
0.5169	16.0	580	0.3794	0.8465
0.4394	16.99	616	0.3876	0.8440
0.4616	17.99	652	0.3844	0.8465
0.4983	18.98	688	0.3552	0.8591
0.5295	20.0	725	0.3561	0.8547
0.5121	20.99	761	0.3573	0.8537
0.4379	21.99	797	0.3593	0.8576
0.4653	22.98	833	0.3473	0.8601
0.486	24.0	870	0.3473	0.8610
0.4751	24.99	906	0.3638	0.8552
0.4462	25.99	942	0.3533	0.8542
0.4197	26.98	978	0.3464	0.8601
0.4966	28.0	1015	0.3451	0.8649
0.5004	28.99	1051	0.3634	0.8508
0.4156	29.99	1087	0.3723	0.8474
0.4508	30.98	1123	0.3342	0.8669
0.43	32.0	1160	0.3389	0.8639
0.5004	32.99	1196	0.3416	0.8615
0.4927	33.99	1232	0.3545	0.8533
0.4802	34.98	1268	0.3382	0.8610
0.4334	36.0	1305	0.3480	0.8542
0.4557	36.99	1341	0.3392	0.8601
0.4551	37.99	1377	0.3488	0.8542
0.4643	38.98	1413	0.3424	0.8586
0.513	39.72	1440	0.3433	0.8576

Framework versions

• Transformers 4.28.0
• Pytorch 1.8.1+cu111
• Datasets 2.14.5
• Tokenizers 0.13.3