SegFormer_PADDING_NLM

AhamadShaik/SegFormer_PADDING_NLM

SegFormer_PADDING_NLM is a machine learning model from AhamadShaik. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Apr 11, 2023
Updated: Apr 11, 2023
SHA: 241d2627

Model card

From the Hugging Face model README

AhamadShaik/SegFormer_PADDING_NLM

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

Train Loss: 0.0161
Train Dice Coef: 0.8042
Train Iou: 0.6813
Validation Loss: 0.0228
Validation Dice Coef: 0.8559
Validation Iou: 0.7502
Train Lr: 1e-10
Epoch: 99

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:

optimizer: {'name': 'Adam', 'learning_rate': 1e-10, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Train Dice Coef	Train Iou	Validation Loss	Validation Dice Coef	Validation Iou	Train Lr	Epoch
0.2695	0.2707	0.1687	0.1167	0.5713	0.4080	1e-04	0
0.0805	0.4647	0.3202	0.0690	0.6569	0.4990	1e-04	1
0.0582	0.5595	0.4069	0.0509	0.7745	0.6369	1e-04	2
0.0507	0.5971	0.4430	0.0459	0.7780	0.6420	1e-04	3
0.0443	0.6379	0.4837	0.0393	0.8008	0.6709	1e-04	4
0.0417	0.6535	0.5011	0.0363	0.8131	0.6882	1e-04	5
0.0384	0.6728	0.5223	0.0355	0.8240	0.7031	1e-04	6
0.0340	0.7019	0.5531	0.0327	0.8297	0.7113	1e-04	7
0.0339	0.7002	0.5530	0.0319	0.8344	0.7182	1e-04	8
0.0315	0.7137	0.5680	0.0307	0.8333	0.7169	1e-04	9
0.0292	0.7341	0.5908	0.0296	0.8400	0.7266	1e-04	10
0.0285	0.7321	0.5905	0.0294	0.8412	0.7279	1e-04	11
0.0287	0.7323	0.5901	0.0310	0.8338	0.7185	1e-04	12
0.0283	0.7361	0.5943	0.0285	0.8457	0.7351	1e-04	13
0.0254	0.7503	0.6120	0.0265	0.8474	0.7376	1e-04	14
0.0239	0.7623	0.6271	0.0262	0.8483	0.7393	1e-04	15
0.0237	0.7589	0.6231	0.0286	0.8425	0.7311	1e-04	16
0.0227	0.7682	0.6339	0.0264	0.8467	0.7368	1e-04	17
0.0236	0.7639	0.6281	0.0273	0.8450	0.7356	1e-04	18
0.0221	0.7701	0.6359	0.0251	0.8491	0.7411	1e-04	19
0.0235	0.7619	0.6262	0.0264	0.8482	0.7394	1e-04	20
0.0216	0.7718	0.6383	0.0257	0.8506	0.7425	1e-04	21
0.0222	0.7715	0.6379	0.0242	0.8543	0.7479	1e-04	22
0.0203	0.7810	0.6500	0.0239	0.8556	0.7499	1e-04	23
0.0196	0.7853	0.6556	0.0241	0.8552	0.7494	1e-04	24
0.0197	0.7813	0.6512	0.0236	0.8529	0.7464	1e-04	25
0.0191	0.7890	0.6608	0.0239	0.8574	0.7525	1e-04	26
0.0180	0.7930	0.6662	0.0236	0.8574	0.7524	1e-04	27
0.0179	0.7973	0.6709	0.0247	0.8559	0.7504	1e-04	28
0.0176	0.7959	0.6702	0.0245	0.8567	0.7515	1e-04	29
0.0225	0.7712	0.6380	0.0245	0.8496	0.7413	1e-04	30
0.0190	0.7867	0.6579	0.0232	0.8546	0.7486	5e-06	31
0.0178	0.7963	0.6701	0.0239	0.8520	0.7450	5e-06	32
0.0175	0.7964	0.6703	0.0232	0.8547	0.7489	5e-06	33
0.0173	0.7972	0.6719	0.0225	0.8556	0.7499	5e-06	34
0.0168	0.8006	0.6759	0.0232	0.8561	0.7507	5e-06	35
0.0167	0.7984	0.6739	0.0232	0.8543	0.7480	5e-06	36
0.0167	0.8025	0.6786	0.0226	0.8561	0.7507	5e-06	37
0.0163	0.8027	0.6791	0.0231	0.8560	0.7505	5e-06	38
0.0164	0.7995	0.6753	0.0225	0.8568	0.7516	5e-06	39
0.0160	0.8077	0.6853	0.0229	0.8559	0.7503	2.5e-07	40
0.0160	0.8070	0.6843	0.0227	0.8567	0.7514	2.5e-07	41
0.0160	0.8072	0.6844	0.0232	0.8564	0.7509	2.5e-07	42
0.0164	0.8037	0.6801	0.0219	0.8573	0.7525	2.5e-07	43
0.0161	0.8022	0.6786	0.0233	0.8548	0.7487	2.5e-07	44
0.0160	0.8041	0.6809	0.0226	0.8571	0.7522	2.5e-07	45
0.0161	0.8043	0.6810	0.0228	0.8558	0.7501	2.5e-07	46
0.0167	0.8035	0.6807	0.0226	0.8564	0.7510	2.5e-07	47
0.0161	0.8047	0.6818	0.0224	0.8565	0.7512	2.5e-07	48
0.0161	0.8041	0.6811	0.0225	0.8576	0.7528	1.25e-08	49
0.0162	0.8060	0.6832	0.0233	0.8555	0.7498	1.25e-08	50
0.0161	0.8029	0.6802	0.0227	0.8570	0.7519	1.25e-08	51
0.0160	0.8069	0.6843	0.0231	0.8556	0.7498	1.25e-08	52
0.0158	0.8074	0.6853	0.0230	0.8567	0.7514	1.25e-08	53
0.0160	0.8050	0.6821	0.0229	0.8563	0.7509	6.25e-10	54
0.0159	0.8057	0.6834	0.0230	0.8558	0.7501	6.25e-10	55
0.0160	0.8045	0.6811	0.0226	0.8574	0.7525	6.25e-10	56
0.0161	0.8061	0.6828	0.0226	0.8560	0.7506	6.25e-10	57
0.0160	0.8062	0.6833	0.0231	0.8560	0.7505	6.25e-10	58
0.0159	0.8037	0.6810	0.0227	0.8557	0.7500	1e-10	59
0.0159	0.8085	0.6859	0.0223	0.8572	0.7522	1e-10	60
0.0159	0.8046	0.6815	0.0223	0.8578	0.7533	1e-10	61
0.0161	0.8057	0.6830	0.0229	0.8561	0.7507	1e-10	62
0.0160	0.8046	0.6817	0.0230	0.8566	0.7514	1e-10	63
0.0160	0.8038	0.6812	0.0225	0.8567	0.7514	1e-10	64
0.0159	0.8084	0.6865	0.0225	0.8561	0.7508	1e-10	65
0.0161	0.8030	0.6795	0.0226	0.8570	0.7520	1e-10	66
0.0163	0.8046	0.6813	0.0223	0.8579	0.7534	1e-10	67
0.0161	0.8047	0.6812	0.0226	0.8566	0.7513	1e-10	68
0.0161	0.8047	0.6813	0.0230	0.8564	0.7510	1e-10	69
0.0164	0.8049	0.6820	0.0231	0.8553	0.7493	1e-10	70
0.0160	0.8041	0.6812	0.0227	0.8574	0.7525	1e-10	71
0.0161	0.8074	0.6852	0.0222	0.8575	0.7527	1e-10	72
0.0161	0.7989	0.6748	0.0228	0.8563	0.7509	1e-10	73
0.0161	0.8009	0.6772	0.0224	0.8569	0.7518	1e-10	74
0.0160	0.8039	0.6803	0.0228	0.8561	0.7506	1e-10	75
0.0160	0.8049	0.6823	0.0222	0.8578	0.7532	1e-10	76
0.0159	0.8053	0.6830	0.0224	0.8565	0.7513	1e-10	77
0.0161	0.8060	0.6825	0.0225	0.8574	0.7525	1e-10	78
0.0159	0.8051	0.6825	0.0227	0.8561	0.7506	1e-10	79
0.0161	0.8034	0.6798	0.0227	0.8559	0.7504	1e-10	80
0.0163	0.8076	0.6850	0.0230	0.8569	0.7517	1e-10	81
0.0159	0.8068	0.6840	0.0234	0.8562	0.7506	1e-10	82
0.0161	0.8047	0.6818	0.0228	0.8562	0.7508	1e-10	83
0.0162	0.8036	0.6802	0.0224	0.8563	0.7509	1e-10	84
0.0160	0.8045	0.6816	0.0230	0.8566	0.7514	1e-10	85
0.0161	0.8026	0.6794	0.0233	0.8562	0.7508	1e-10	86
0.0161	0.8036	0.6806	0.0228	0.8562	0.7507	1e-10	87
0.0160	0.8026	0.6791	0.0230	0.8556	0.7497	1e-10	88
0.0164	0.8041	0.6809	0.0227	0.8564	0.7509	1e-10	89
0.0163	0.8029	0.6797	0.0232	0.8552	0.7493	1e-10	90
0.0161	0.8061	0.6833	0.0231	0.8553	0.7495	1e-10	91
0.0160	0.8043	0.6814	0.0222	0.8577	0.7530	1e-10	92
0.0159	0.8058	0.6833	0.0223	0.8560	0.7505	1e-10	93
0.0161	0.8032	0.6796	0.0223	0.8566	0.7514	1e-10	94
0.0162	0.8037	0.6804	0.0228	0.8558	0.7502	1e-10	95
0.0160	0.8037	0.6805	0.0226	0.8568	0.7517	1e-10	96
0.0160	0.8059	0.6835	0.0223	0.8579	0.7533	1e-10	97
0.0160	0.8055	0.6822	0.0229	0.8552	0.7492	1e-10	98
0.0161	0.8042	0.6813	0.0228	0.8559	0.7502	1e-10	99

Framework versions

Transformers 4.27.4
TensorFlow 2.10.1
Datasets 2.11.0
Tokenizers 0.13.3

Library: transformers
Type: segformer
License: other
Created: Apr 11, 2023
Updated: Apr 11, 2023