sagittal-b0-finetuned-segments

This model is a fine-tuned version of nvidia/mit-b0 on the jenniferlumeng/Sagittal dataset. It achieves the following results on the evaluation set:

• Loss: 1.0561
• Mean Iou: 0.3928
• Mean Accuracy: 0.5550
• Overall Accuracy: 0.6001
• Accuracy Background: nan
• Accuracy Olfactory bulb: 0.7548
• Accuracy Anterior olfactory nucleus: 0.2361
• Accuracy Basal ganglia: 0.5670
• Accuracy Cortex: 0.8443
• Accuracy Hypothalamus: 0.3500
• Accuracy Thalamus: 0.3216
• Accuracy Hippocampus: 0.4568
• Accuracy Midbrain: 0.7339
• Accuracy Cerebellum: 0.8112
• Accuracy Pons and medulla: 0.4748
• Iou Background: 0.0
• Iou Olfactory bulb: 0.5861
• Iou Anterior olfactory nucleus: 0.2110
• Iou Basal ganglia: 0.4574
• Iou Cortex: 0.6560
• Iou Hypothalamus: 0.3196
• Iou Thalamus: 0.3020
• Iou Hippocampus: 0.4364
• Iou Midbrain: 0.2970
• Iou Cerebellum: 0.6248
• Iou Pons and medulla: 0.4303

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: 6e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Olfactory bulb	Accuracy Anterior olfactory nucleus	Accuracy Basal ganglia	Accuracy Cortex	Accuracy Hypothalamus	Accuracy Thalamus	Accuracy Hippocampus	Accuracy Midbrain	Accuracy Cerebellum	Accuracy Pons and medulla	Iou Background	Iou Olfactory bulb	Iou Anterior olfactory nucleus	Iou Basal ganglia	Iou Cortex	Iou Hypothalamus	Iou Thalamus	Iou Hippocampus	Iou Midbrain	Iou Cerebellum	Iou Pons and medulla
1.9251	3.3333	20	2.3067	0.0795	0.1796	0.2634	nan	0.2399	0.0	0.0	0.9926	0.0554	0.0	0.0957	0.0	0.3004	0.1122	0.0	0.1546	0.0	0.0	0.2430	0.0548	0.0	0.0692	0.0	0.2769	0.0760
1.5773	6.6667	40	1.8563	0.1695	0.2863	0.3584	nan	0.4130	0.0	0.0340	0.8260	0.2004	0.1704	0.1409	0.4293	0.4427	0.2064	0.0	0.2795	0.0	0.0326	0.5150	0.1849	0.1148	0.1249	0.1197	0.3308	0.1619
1.3176	10.0	60	1.6904	0.2571	0.4066	0.4408	nan	0.6887	0.0	0.8044	0.7641	0.2494	0.1046	0.3613	0.2678	0.5775	0.2483	0.0	0.4874	0.0	0.1599	0.6414	0.2309	0.0979	0.3281	0.2014	0.4568	0.2239
1.3147	13.3333	80	1.4640	0.2817	0.4272	0.4773	nan	0.5533	0.0	0.4670	0.7442	0.2610	0.0613	0.4597	0.6721	0.6021	0.4509	0.0	0.3820	0.0	0.3403	0.6051	0.2377	0.0604	0.4191	0.2155	0.4920	0.3467
1.1284	16.6667	100	1.3582	0.2754	0.4165	0.4699	nan	0.6502	0.0036	0.5064	0.8678	0.2266	0.1352	0.4677	0.2578	0.5931	0.4562	0.0	0.5197	0.0036	0.3552	0.3330	0.2138	0.1324	0.4201	0.2191	0.4907	0.3414
1.1223	20.0	120	1.2891	0.2862	0.4221	0.4689	nan	0.6084	0.1211	0.3576	0.8239	0.2694	0.1435	0.4589	0.4033	0.5980	0.4369	0.0	0.4424	0.1137	0.3147	0.3247	0.2529	0.1306	0.4291	0.3035	0.4722	0.3644
1.0746	23.3333	140	1.2653	0.3122	0.4564	0.5064	nan	0.5209	0.0778	0.5140	0.7894	0.3297	0.1814	0.4610	0.7193	0.5320	0.4385	0.0	0.4051	0.0749	0.4002	0.5456	0.2988	0.1715	0.4247	0.2543	0.4697	0.3895
1.1374	26.6667	160	1.2168	0.3412	0.5006	0.5445	nan	0.7152	0.1454	0.5457	0.8318	0.3455	0.2429	0.4675	0.6257	0.6441	0.4424	0.0	0.5520	0.1348	0.3895	0.5159	0.3081	0.2310	0.4254	0.2683	0.5242	0.4046
0.8899	30.0	180	1.1327	0.3517	0.4974	0.5497	nan	0.5466	0.1382	0.4970	0.7591	0.3350	0.2808	0.4335	0.7737	0.7053	0.5047	0.0	0.4196	0.1286	0.4157	0.6405	0.3087	0.2560	0.4186	0.2777	0.5777	0.4251
0.822	33.3333	200	1.1223	0.3765	0.5371	0.5878	nan	0.7811	0.1923	0.5470	0.8318	0.3197	0.2845	0.4424	0.7182	0.6918	0.5623	0.0	0.5922	0.1736	0.4012	0.6352	0.3021	0.2676	0.4264	0.2960	0.5843	0.4632
1.3568	36.6667	220	1.0941	0.4136	0.5818	0.6319	nan	0.8136	0.2270	0.5548	0.8444	0.3559	0.7737	0.4477	0.4848	0.7838	0.5318	0.0	0.6210	0.2027	0.4504	0.6852	0.3245	0.4370	0.4324	0.2808	0.6397	0.4756
0.9664	40.0	240	1.0685	0.3843	0.5490	0.5962	nan	0.6310	0.2199	0.5723	0.8041	0.3211	0.8415	0.4591	0.3971	0.7908	0.4532	0.0	0.4706	0.1978	0.4563	0.6778	0.3002	0.4135	0.4364	0.2563	0.6112	0.4073
0.824	43.3333	260	1.0399	0.3955	0.5580	0.6051	nan	0.7486	0.2461	0.5592	0.8658	0.3265	0.5614	0.4605	0.5438	0.8004	0.4678	0.0	0.5838	0.2191	0.4581	0.6516	0.3069	0.3773	0.4371	0.2778	0.6095	0.4297
1.126	46.6667	280	1.0414	0.4036	0.5741	0.6232	nan	0.7521	0.2361	0.5644	0.8473	0.3231	0.8344	0.4528	0.4500	0.8079	0.4731	0.0	0.5863	0.2112	0.4592	0.6156	0.3047	0.4545	0.4341	0.3238	0.6222	0.4280
0.8935	50.0	300	1.0561	0.3928	0.5550	0.6001	nan	0.7548	0.2361	0.5670	0.8443	0.3500	0.3216	0.4568	0.7339	0.8112	0.4748	0.0	0.5861	0.2110	0.4574	0.6560	0.3196	0.3020	0.4364	0.2970	0.6248	0.4303

Framework versions

• Transformers 4.52.2
• Pytorch 2.6.0+cu124
• Datasets 2.16.1
• Tokenizers 0.21.1