dropoff-utcustom-train-SF-RGBD-b0_6

This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set:

• Loss: 0.2353
• Mean Iou: 0.6539
• Mean Accuracy: 0.7065
• Overall Accuracy: 0.9662
• Accuracy Unlabeled: nan
• Accuracy Dropoff: 0.4233
• Accuracy Undropoff: 0.9897
• Iou Unlabeled: nan
• Iou Dropoff: 0.3423
• Iou Undropoff: 0.9656

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: 7e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Unlabeled	Accuracy Dropoff	Accuracy Undropoff	Iou Unlabeled	Iou Dropoff	Iou Undropoff
0.9975	5.0	10	1.0470	0.2819	0.6747	0.7186	nan	0.6267	0.7226	0.0	0.1290	0.7167
0.8329	10.0	20	0.8435	0.3211	0.5026	0.9526	nan	0.0117	0.9934	0.0	0.0106	0.9526
0.6857	15.0	30	0.6184	0.3191	0.4994	0.9567	nan	0.0006	0.9981	0.0	0.0006	0.9567
0.5913	20.0	40	0.4793	0.3193	0.4997	0.9573	nan	0.0005	0.9988	0.0	0.0005	0.9573
0.5299	25.0	50	0.4529	0.3488	0.5442	0.9596	nan	0.0911	0.9973	0.0	0.0869	0.9595
0.4922	30.0	60	0.4037	0.4352	0.6983	0.9671	nan	0.4051	0.9915	0.0	0.3390	0.9666
0.4769	35.0	70	0.4161	0.4090	0.7560	0.9426	nan	0.5524	0.9595	0.0	0.2858	0.9412
0.3916	40.0	80	0.3343	0.6320	0.6946	0.9614	nan	0.4036	0.9856	nan	0.3033	0.9608
0.3567	45.0	90	0.3372	0.6374	0.7140	0.9598	nan	0.4458	0.9821	nan	0.3157	0.9591
0.3234	50.0	100	0.3074	0.6402	0.6883	0.9652	nan	0.3863	0.9903	nan	0.3157	0.9646
0.3181	55.0	110	0.3043	0.6396	0.7138	0.9606	nan	0.4446	0.9830	nan	0.3194	0.9599
0.2584	60.0	120	0.3069	0.6450	0.7204	0.9613	nan	0.4576	0.9831	nan	0.3294	0.9605
0.2566	65.0	130	0.2824	0.6431	0.7063	0.9630	nan	0.4263	0.9863	nan	0.3239	0.9623
0.2353	70.0	140	0.2763	0.6470	0.7046	0.9645	nan	0.4212	0.9880	nan	0.3301	0.9638
0.2368	75.0	150	0.2644	0.6474	0.6973	0.9658	nan	0.4044	0.9902	nan	0.3296	0.9652
0.2225	80.0	160	0.2673	0.6462	0.7089	0.9635	nan	0.4313	0.9866	nan	0.3296	0.9629
0.1976	85.0	170	0.2568	0.6449	0.7057	0.9637	nan	0.4244	0.9870	nan	0.3268	0.9630
0.1981	90.0	180	0.2572	0.6444	0.7110	0.9626	nan	0.4365	0.9855	nan	0.3269	0.9619
0.1857	95.0	190	0.2503	0.6504	0.7027	0.9658	nan	0.4157	0.9897	nan	0.3356	0.9652
0.1826	100.0	200	0.2345	0.6509	0.6984	0.9666	nan	0.4059	0.9909	nan	0.3357	0.9660
0.1818	105.0	210	0.2484	0.6506	0.7160	0.9637	nan	0.4458	0.9862	nan	0.3381	0.9630
0.1919	110.0	220	0.2343	0.6526	0.6996	0.9669	nan	0.4080	0.9912	nan	0.3389	0.9663
0.17	115.0	230	0.2377	0.6535	0.7065	0.9661	nan	0.4235	0.9896	nan	0.3416	0.9655
0.1739	120.0	240	0.2353	0.6539	0.7065	0.9662	nan	0.4233	0.9897	nan	0.3423	0.9656

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3