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badinkajink/
segformer-webots-grasp

Name: segformer-webots-grasp
Availability: InStock

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segformer-webots-grasp

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

Loss: 4.4405
Mean Iou: 0.0802
Mean Accuracy: 0.8142
Overall Accuracy: 0.8142
Per Category Iou: [0.8018109121927621, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]
Per Category Accuracy: [0.8141941463133944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]

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: 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	Per Category Iou	Per Category Accuracy
5.4277	0.03	1	5.4055	0.0001	0.0011	0.0011	[0.0010665493208110168, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.001069721346006343, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.3872	0.05	2	5.3965	0.0004	0.0053	0.0053	[0.005245532234607124, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.005252500148444879, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.3659	0.07	3	5.3862	0.0013	0.0160	0.0160	[0.01595653583377678, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.015969302211124733, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.2685	0.1	4	5.3741	0.0037	0.0445	0.0445	[0.04445613702342968, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.044479595103583916, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.2464	0.12	5	5.3603	0.0113	0.1359	0.1359	[0.13585817440138379, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.1359222528362154, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.1881	0.15	6	5.3461	0.0240	0.2880	0.2880	[0.287886510883373, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.2879980631769163, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.1556	0.17	7	5.3322	0.0329	0.3949	0.3949	[0.3948099641784779, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.3949236748463366, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.0952	0.2	8	5.3175	0.0400	0.4807	0.4807	[0.4805425893875233, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]	[0.4807401186212317, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.0421	0.23	9	5.3015	0.0606	0.5455	0.5455	[0.5450016850554845, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.5454547402275828, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
5.0168	0.25	10	5.2843	0.0659	0.5936	0.5936	[0.5926648159557198, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.5935808761369806, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.9417	0.28	11	5.2641	0.0575	0.6341	0.6341	[0.6324597013631272, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.6340619477314868, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.8962	0.3	12	5.2394	0.0606	0.6696	0.6696	[0.6670764824873869, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.6695910817989438, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.9963	0.33	13	5.2132	0.0574	0.6918	0.6918	[0.6887950316615776, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.6918253700562499, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.9677	0.35	14	5.1887	0.0703	0.7061	0.7061	[0.7026749604186757, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7060962792061474, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.921	0.38	15	5.1585	0.0799	0.7228	0.7228	[0.7188799212945968, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7228307612069762, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.9849	0.4	16	5.1298	0.0808	0.7311	0.7311	[0.7268635588232967, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7311038850932571, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.8045	0.42	17	5.0921	0.0823	0.7458	0.7458	[0.7409411768283134, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7458280867505752, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.6795	0.45	18	5.0554	0.0835	0.7575	0.7575	[0.7518766884727517, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7574526981157601, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.7354	0.47	19	5.0164	0.0848	0.7698	0.7698	[0.7632263888994257, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7697950481236874, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.64	0.5	20	4.9783	0.0771	0.7779	0.7779	[0.7705429510590344, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7778955474494109, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.6682	0.53	21	4.9336	0.0777	0.7858	0.7858	[0.7773627351835143, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7858173007762596, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.6887	0.55	22	4.8904	0.0782	0.7914	0.7914	[0.7821032704184515, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.791440760086753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5486	0.57	23	4.8541	0.0785	0.7943	0.7943	[0.7845670293951555, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7943371186267411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.6823	0.6	24	4.8202	0.0787	0.7972	0.7972	[0.786876415221171, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.797181751012945, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5797	0.62	25	4.7832	0.0789	0.8001	0.8001	[0.7893493389876929, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8000900463576524, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5976	0.65	26	4.7564	0.0789	0.7999	0.7999	[0.7892736529850544, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.7998681442186382, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5417	0.68	27	4.7152	0.0793	0.8040	0.8040	[0.7927347074267436, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8040157247507503, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.608	0.7	28	4.6820	0.0794	0.8053	0.8053	[0.7938510702940416, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8053333643486208, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4955	0.72	29	4.6511	0.0795	0.8062	0.8062	[0.7946513690612669, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8061980175109864, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5359	0.75	30	4.6301	0.0794	0.8057	0.8057	[0.7942641352298992, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8056544337883806, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4116	0.78	31	4.5990	0.0797	0.8094	0.8094	[0.7974774354228353, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8094237094324631, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4995	0.8	32	4.5779	0.0798	0.8098	0.8098	[0.7978091719139048, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8097701828412411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5087	0.82	33	4.5585	0.0799	0.8112	0.8112	[0.7989926828268309, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8112010690479878, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4534	0.85	34	4.5346	0.0799	0.8116	0.8116	[0.7994616833790221, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8116476279732591, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4347	0.88	35	4.5080	0.0800	0.8121	0.8121	[0.7999173944895492, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8121012265525958, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4389	0.9	36	4.4889	0.0800	0.8119	0.8119	[0.7998204784246967, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8119105437490014, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.428	0.93	37	4.4843	0.0799	0.8105	0.8105	[0.7987525641385264, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8105448508603376, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.5333	0.95	38	4.4847	0.0798	0.8094	0.8094	[0.7978741861189972, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8093536189637262, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4458	0.97	39	4.4634	0.0798	0.8098	0.8098	[0.7981748026222505, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8097799771425493, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.4749	1.0	40	4.4405	0.0802	0.8142	0.8142	[0.8018109121927621, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]	[0.8141941463133944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

badinkajink/segformer-webots-grasp

segformer-webots-grasp

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

badinkajink/
segformer-webots-grasp