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guohao123/
nvidia-mit-b3_finetune_sidewalk-semantic

Name: nvidia-mit-b3_finetune_sidewalk-semantic
Availability: InStock

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nvidia-mit-b3_finetune_sidewalk-semantic

This model is a fine-tuned version of nvidia/mit-b3 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

Loss: 0.7270
Mean Iou: 0.2391
Mean Accuracy: 0.2831
Overall Accuracy: 0.8089
Acc Unlabeled: nan
Acc Flat-road: 0.8462
Acc Flat-sidewalk: 0.9578
Acc Flat-crosswalk: 0.0
Acc Flat-cyclinglane: 0.4756
Acc Flat-parkingdriveway: 0.2145
Acc Flat-railtrack: nan
Acc Flat-curb: 0.3585
Acc Human-person: 0.2813
Acc Human-rider: 0.0
Acc Vehicle-car: 0.9514
Acc Vehicle-truck: 0.0
Acc Vehicle-bus: 0.0
Acc Vehicle-tramtrain: 0.0
Acc Vehicle-motorcycle: 0.0
Acc Vehicle-bicycle: 0.2160
Acc Vehicle-caravan: 0.0
Acc Vehicle-cartrailer: 0.0
Acc Construction-building: 0.9204
Acc Construction-door: 0.0
Acc Construction-wall: 0.2528
Acc Construction-fenceguardrail: 0.3314
Acc Construction-bridge: 0.0
Acc Construction-tunnel: nan
Acc Construction-stairs: 0.0
Acc Object-pole: 0.3098
Acc Object-trafficsign: 0.0
Acc Object-trafficlight: 0.0
Acc Nature-vegetation: 0.9375
Acc Nature-terrain: 0.8768
Acc Sky: 0.9406
Acc Void-ground: 0.0
Acc Void-dynamic: 0.0
Acc Void-static: 0.1895
Acc Void-unclear: 0.0
Iou Unlabeled: nan
Iou Flat-road: 0.6618
Iou Flat-sidewalk: 0.8235
Iou Flat-crosswalk: 0.0
Iou Flat-cyclinglane: 0.4373
Iou Flat-parkingdriveway: 0.1762
Iou Flat-railtrack: nan
Iou Flat-curb: 0.2784
Iou Human-person: 0.2378
Iou Human-rider: 0.0
Iou Vehicle-car: 0.7833
Iou Vehicle-truck: 0.0
Iou Vehicle-bus: 0.0
Iou Vehicle-tramtrain: 0.0
Iou Vehicle-motorcycle: 0.0
Iou Vehicle-bicycle: 0.2063
Iou Vehicle-caravan: 0.0
Iou Vehicle-cartrailer: 0.0
Iou Construction-building: 0.6478
Iou Construction-door: 0.0
Iou Construction-wall: 0.2196
Iou Construction-fenceguardrail: 0.3081
Iou Construction-bridge: 0.0
Iou Construction-tunnel: nan
Iou Construction-stairs: 0.0
Iou Object-pole: 0.2490
Iou Object-trafficsign: 0.0
Iou Object-trafficlight: 0.0
Iou Nature-vegetation: 0.8362
Iou Nature-terrain: 0.7356
Iou Sky: 0.8856
Iou Void-ground: 0.0
Iou Void-dynamic: 0.0
Iou Void-static: 0.1633
Iou Void-unclear: 0.0

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: 3
eval_batch_size: 3
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Acc Unlabeled	Acc Flat-road	Acc Flat-sidewalk	Acc Flat-cyclinglane	Acc Flat-parkingdriveway	Acc Flat-railtrack	Acc Flat-curb	Acc Human-person	Acc Vehicle-car	Acc Vehicle-bicycle	Acc Construction-building	Acc Construction-wall	Acc Construction-fenceguardrail	Acc Construction-tunnel	Acc Object-pole	Acc Nature-vegetation	Acc Nature-terrain	Acc Sky	Acc Void-static	Iou Unlabeled	Iou Flat-road	Iou Flat-sidewalk	Iou Flat-cyclinglane	Iou Flat-parkingdriveway	Iou Flat-railtrack	Iou Flat-curb	Iou Human-person	Iou Vehicle-car	Iou Vehicle-bicycle	Iou Construction-building	Iou Construction-wall	Iou Construction-fenceguardrail	Iou Construction-tunnel	Iou Object-pole	Iou Nature-vegetation	Iou Nature-terrain	Iou Sky	Iou Void-static
1.2696	0.14	33	1.3198	0.1366	0.1799	0.7212	nan	0.7268	0.9591	0.0	0.0054	nan	0.0	0.0	0.9144	0.0	0.8191	0.0032	0.0002	nan	0.0	0.9455	0.5741	0.8065	0.0014	nan	0.5092	0.7200	0.0	0.0052	nan	0.0	0.0	0.7334	0.0	0.5417	0.0032	0.0002	0.0	0.0	0.7237	0.4871	0.7824	0.0014
1.196	0.28	66	0.9744	0.1660	0.2092	0.7621	nan	0.8403	0.9446	0.2749	0.0164	nan	0.0000	0.0	0.9510	0.0	0.8910	0.0108	0.0620	nan	0.0256	0.9337	0.8260	0.9169	0.0018	nan	0.5760	0.7791	0.2646	0.0159	nan	0.0000	0.0	0.7093	0.0	0.5887	0.0107	0.0618	nan	0.0253	0.7845	0.6386	0.8554	0.0018
1.3009	0.42	99	0.8567	0.1940	0.2355	0.7789	nan	0.8392	0.9534	0.3587	0.0450	nan	0.0226	0.0861	0.9475	0.0085	0.9180	0.2409	0.2594	nan	0.1095	0.9179	0.8459	0.9262	0.0561	nan	0.5901	0.7918	0.3215	0.0422	nan	0.0221	0.0815	0.7401	0.0085	0.6252	0.2113	0.2513	nan	0.1028	0.8096	0.6881	0.8659	0.0543
0.8802	0.56	132	0.8121	0.2111	0.2533	0.7870	nan	0.7684	0.9678	0.5098	0.0480	nan	0.1309	0.1147	0.9545	0.1207	0.8997	0.3134	0.2572	nan	0.2145	0.9277	0.8730	0.9489	0.0552	nan	0.6078	0.7799	0.4300	0.0452	nan	0.1190	0.1086	0.7451	0.1171	0.6525	0.2578	0.2467	nan	0.1851	0.8146	0.7176	0.8735	0.0531
0.6118	0.71	165	0.7667	0.2267	0.2697	0.7976	nan	0.8872	0.9438	0.4101	0.1549	nan	0.1201	0.2090	0.9404	0.1452	0.8688	0.3950	0.3483	nan	0.2586	0.9422	0.8637	0.9486	0.1931	nan	0.6038	0.8210	0.3802	0.1356	nan	0.1094	0.1871	0.7863	0.1413	0.6519	0.2892	0.3200	nan	0.2204	0.8268	0.7340	0.8818	0.1644
1.3374	0.85	198	0.7325	0.2339	0.2768	0.8064	nan	0.8797	0.9501	0.4559	0.2181	nan	0.2268	0.2519	0.9457	0.2066	0.9112	0.2305	0.3397	nan	0.2854	0.9403	0.8881	0.9445	0.1843	nan	0.6533	0.8263	0.4195	0.1737	nan	0.1954	0.2214	0.7893	0.1979	0.6490	0.2057	0.3186	nan	0.2353	0.8301	0.7274	0.8832	0.1599
0.9023	0.99	231	0.7270	0.2391	0.2831	0.8089	nan	0.8462	0.9578	0.4756	0.2145	nan	0.3585	0.2813	0.9514	0.2160	0.9204	0.2528	0.3314	nan	0.3098	0.9375	0.8768	0.9406	0.1895	nan	0.6618	0.8235	0.4373	0.1762	nan	0.2784	0.2378	0.7833	0.2063	0.6478	0.2196	0.3081	nan	0.2490	0.8362	0.7356	0.8856	0.1633

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu117
Datasets 2.20.0
Tokenizers 0.13.3

guohao123/nvidia-mit-b3_finetune_sidewalk-semantic

nvidia-mit-b3_finetune_sidewalk-semantic

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

guohao123/
nvidia-mit-b3_finetune_sidewalk-semantic