chiHang/rowbody-4cats-outputs

segformer-b0-finetuned-segments-rowbody-4cats

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1486
• Mean Iou: 0.6527
• Mean Accuracy: 0.9381
• Overall Accuracy: 0.9558
• Accuracy Sleeve-right: nan
• Accuracy Sleeve-left: 0.9259
• Accuracy Neck: 0.9212
• Accuracy Body: 0.9670
• Iou Sleeve-right: 0.0
• Iou Sleeve-left: 0.9012
• Iou Neck: 0.7545
• Iou Body: 0.9551

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Sleeve-right	Accuracy Sleeve-left	Accuracy Neck	Accuracy Body	Iou Sleeve-right	Iou Sleeve-left	Iou Neck	Iou Body
0.9629	2.5	20	1.1228	0.2325	0.3900	0.7680	nan	0.1708	0.0	0.9991	0.0	0.1703	0.0	0.7598
0.6667	5.0	40	0.6465	0.4513	0.6646	0.8681	nan	0.5731	0.4337	0.9870	0.0	0.5659	0.3911	0.8483
0.414	7.5	60	0.4340	0.5935	0.8506	0.9377	nan	0.8496	0.7215	0.9806	0.0	0.8374	0.6136	0.9228
0.3351	10.0	80	0.3371	0.6263	0.9237	0.9411	nan	0.9325	0.8909	0.9478	0.0	0.8568	0.7171	0.9314
0.4849	12.5	100	0.3146	0.6428	0.9438	0.9526	nan	0.9708	0.9093	0.9513	0.0	0.8922	0.7375	0.9415
0.2461	15.0	120	0.2716	0.6431	0.9554	0.9496	nan	0.9717	0.9511	0.9434	0.0	0.8938	0.7410	0.9373
0.2123	17.5	140	0.2477	0.6715	0.9481	0.9679	nan	0.9523	0.9151	0.9768	0.0	0.9229	0.7999	0.9632
0.1827	20.0	160	0.2413	0.6620	0.9048	0.9649	nan	0.9200	0.8032	0.9912	0.0	0.9147	0.7717	0.9617
0.2828	22.5	180	0.2286	0.6484	0.9472	0.9532	nan	0.9622	0.9265	0.9530	0.0	0.8996	0.7495	0.9443
0.4631	25.0	200	0.2137	0.6459	0.9452	0.9485	nan	0.9523	0.9345	0.9486	0.0	0.8886	0.7543	0.9408
0.159	27.5	220	0.1854	0.6336	0.9374	0.9415	nan	0.9355	0.9328	0.9440	0.0	0.8832	0.7145	0.9368
0.1361	30.0	240	0.1760	0.6563	0.9429	0.9576	nan	0.9492	0.9161	0.9635	0.0	0.9027	0.7687	0.9538
0.1369	32.5	260	0.1634	0.6516	0.9451	0.9562	nan	0.9497	0.9249	0.9607	0.0	0.9130	0.7426	0.9507
0.1356	35.0	280	0.1580	0.6488	0.9248	0.9527	nan	0.9154	0.8905	0.9684	0.0	0.8966	0.7457	0.9528
0.2415	37.5	300	0.1635	0.6457	0.9439	0.9523	nan	0.9406	0.9340	0.9571	0.0	0.9003	0.7344	0.9482
0.1183	40.0	320	0.1639	0.6463	0.9386	0.9524	nan	0.9286	0.9262	0.9612	0.0	0.8998	0.7351	0.9500
0.1264	42.5	340	0.1564	0.6512	0.9439	0.9552	nan	0.9385	0.9312	0.9618	0.0	0.9066	0.7463	0.9521
0.2418	45.0	360	0.1553	0.6569	0.9366	0.9589	nan	0.9198	0.9166	0.9734	0.0	0.9033	0.7656	0.9587
0.189	47.5	380	0.1611	0.6520	0.9386	0.9544	nan	0.9284	0.9231	0.9643	0.0	0.8990	0.7554	0.9536
0.1132	50.0	400	0.1486	0.6527	0.9381	0.9558	nan	0.9259	0.9212	0.9670	0.0	0.9012	0.7545	0.9551

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2