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AhamadShaik/
SegFormer_PADDING_LM

Name: SegFormer_PADDING_LM
Availability: InStock

transformers

segformer

generated_from_keras_callback

other

endpoints_compatible

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AhamadShaik/SegFormer_PADDING_LM

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: 0.0186
Train Dice Coef: 0.7789
Train Iou: 0.6508
Validation Loss: 0.0233
Validation Dice Coef: 0.8506
Validation Iou: 0.7439
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
0.1460	0.3657	0.2410	0.0908	0.4603	0.3168	1e-04	0
0.0610	0.5251	0.3760	0.1773	0.1542	0.0892	1e-04	1
0.0500	0.5831	0.4322	0.0806	0.5067	0.3659	1e-04	2
0.0432	0.6204	0.4699	0.1085	0.3757	0.2479	1e-04	3
0.0413	0.6306	0.4831	0.0771	0.5239	0.3646	1e-04	4
0.0374	0.6569	0.5086	0.0719	0.5267	0.3854	1e-04	5
0.0336	0.6770	0.5307	0.0540	0.6264	0.4881	1e-04	6
0.0302	0.7029	0.5592	0.0518	0.6516	0.5234	1e-04	7
0.0306	0.7010	0.5582	0.0704	0.5946	0.4483	1e-04	8
0.0285	0.7160	0.5744	0.0504	0.6951	0.5568	1e-04	9
0.0287	0.7245	0.5830	0.0357	0.7899	0.6630	1e-04	10
0.0273	0.7228	0.5825	0.0659	0.6279	0.4914	1e-04	11
0.0259	0.7344	0.5961	0.0357	0.7986	0.6716	1e-04	12
0.0257	0.7405	0.6010	0.0385	0.7970	0.6702	1e-04	13
0.0237	0.7434	0.6076	0.0364	0.8060	0.6841	1e-04	14
0.0227	0.7532	0.6192	0.0556	0.6927	0.5449	1e-04	15
0.0225	0.7546	0.6202	0.0242	0.8446	0.7356	5e-06	16
0.0207	0.7614	0.6312	0.0235	0.8482	0.7406	5e-06	17
0.0205	0.7676	0.6365	0.0235	0.8489	0.7414	5e-06	18
0.0200	0.7689	0.6389	0.0238	0.8497	0.7424	5e-06	19
0.0201	0.7693	0.6384	0.0237	0.8492	0.7418	5e-06	20
0.0195	0.7738	0.6438	0.0231	0.8504	0.7440	5e-06	21
0.0196	0.7749	0.6458	0.0234	0.8504	0.7436	5e-06	22
0.0192	0.7756	0.6464	0.0236	0.8482	0.7407	5e-06	23
0.0191	0.7741	0.6447	0.0231	0.8503	0.7435	5e-06	24
0.0191	0.7761	0.6466	0.0238	0.8493	0.7419	5e-06	25
0.0188	0.7781	0.6503	0.0237	0.8481	0.7405	5e-06	26
0.0192	0.7729	0.6440	0.0234	0.8483	0.7414	2.5e-07	27
0.0187	0.7849	0.6572	0.0241	0.8478	0.7398	2.5e-07	28
0.0188	0.7786	0.6501	0.0241	0.8484	0.7406	2.5e-07	29
0.0189	0.7815	0.6520	0.0232	0.8507	0.7439	2.5e-07	30
0.0185	0.7715	0.6440	0.0232	0.8505	0.7437	2.5e-07	31
0.0186	0.7764	0.6488	0.0233	0.8487	0.7416	1.25e-08	32
0.0189	0.7725	0.6438	0.0235	0.8492	0.7418	1.25e-08	33
0.0186	0.7767	0.6484	0.0237	0.8491	0.7414	1.25e-08	34
0.0186	0.7800	0.6517	0.0229	0.8503	0.7436	1.25e-08	35
0.0187	0.7758	0.6463	0.0232	0.8501	0.7433	1.25e-08	36
0.0187	0.7774	0.6497	0.0232	0.8496	0.7423	1.25e-08	37
0.0187	0.7791	0.6502	0.0234	0.8496	0.7424	1.25e-08	38
0.0189	0.7743	0.6446	0.0237	0.8501	0.7429	1.25e-08	39
0.0189	0.7770	0.6491	0.0234	0.8479	0.7402	1.25e-08	40
0.0187	0.7793	0.6507	0.0233	0.8507	0.7441	6.25e-10	41
0.0186	0.7788	0.6505	0.0231	0.8502	0.7434	6.25e-10	42
0.0188	0.7773	0.6491	0.0232	0.8510	0.7443	6.25e-10	43
0.0185	0.7775	0.6493	0.0229	0.8518	0.7456	6.25e-10	44
0.0187	0.7765	0.6487	0.0233	0.8491	0.7416	6.25e-10	45
0.0186	0.7804	0.6521	0.0234	0.8499	0.7430	1e-10	46
0.0187	0.7765	0.6482	0.0235	0.8486	0.7410	1e-10	47
0.0187	0.7777	0.6497	0.0233	0.8493	0.7419	1e-10	48
0.0187	0.7785	0.6498	0.0230	0.8502	0.7432	1e-10	49
0.0188	0.7813	0.6529	0.0235	0.8491	0.7418	1e-10	50
0.0186	0.7770	0.6498	0.0229	0.8504	0.7435	1e-10	51
0.0190	0.7764	0.6483	0.0232	0.8503	0.7437	1e-10	52
0.0189	0.7764	0.6480	0.0233	0.8500	0.7430	1e-10	53
0.0189	0.7744	0.6461	0.0231	0.8516	0.7449	1e-10	54
0.0188	0.7767	0.6485	0.0233	0.8499	0.7429	1e-10	55
0.0189	0.7729	0.6441	0.0234	0.8488	0.7413	1e-10	56
0.0186	0.7814	0.6531	0.0235	0.8486	0.7408	1e-10	57
0.0189	0.7772	0.6480	0.0237	0.8482	0.7405	1e-10	58
0.0187	0.7756	0.6477	0.0231	0.8511	0.7443	1e-10	59
0.0188	0.7783	0.6500	0.0234	0.8489	0.7415	1e-10	60
0.0186	0.7771	0.6484	0.0238	0.8482	0.7402	1e-10	61
0.0186	0.7776	0.6502	0.0231	0.8499	0.7429	1e-10	62
0.0185	0.7784	0.6504	0.0232	0.8496	0.7422	1e-10	63
0.0188	0.7797	0.6519	0.0234	0.8484	0.7406	1e-10	64
0.0189	0.7851	0.6566	0.0230	0.8518	0.7455	1e-10	65
0.0187	0.7795	0.6515	0.0237	0.8494	0.7420	1e-10	66
0.0188	0.7779	0.6489	0.0237	0.8470	0.7395	1e-10	67
0.0190	0.7751	0.6455	0.0243	0.8472	0.7391	1e-10	68
0.0188	0.7767	0.6486	0.0233	0.8502	0.7433	1e-10	69
0.0189	0.7819	0.6535	0.0231	0.8504	0.7436	1e-10	70
0.0188	0.7734	0.6452	0.0230	0.8508	0.7442	1e-10	71
0.0186	0.7784	0.6516	0.0234	0.8484	0.7414	1e-10	72
0.0187	0.7706	0.6424	0.0236	0.8483	0.7407	1e-10	73
0.0189	0.7720	0.6430	0.0237	0.8481	0.7401	1e-10	74
0.0189	0.7753	0.6464	0.0232	0.8505	0.7439	1e-10	75
0.0188	0.7759	0.6481	0.0232	0.8500	0.7427	1e-10	76
0.0188	0.7760	0.6479	0.0235	0.8494	0.7418	1e-10	77
0.0187	0.7828	0.6538	0.0231	0.8518	0.7456	1e-10	78
0.0188	0.7771	0.6489	0.0235	0.8488	0.7414	1e-10	79
0.0188	0.7766	0.6480	0.0235	0.8487	0.7411	1e-10	80
0.0187	0.7764	0.6492	0.0236	0.8497	0.7421	1e-10	81
0.0188	0.7769	0.6489	0.0232	0.8504	0.7434	1e-10	82
0.0190	0.7805	0.6507	0.0237	0.8494	0.7418	1e-10	83
0.0187	0.7752	0.6473	0.0231	0.8502	0.7431	1e-10	84
0.0189	0.7758	0.6472	0.0234	0.8484	0.7414	1e-10	85
0.0185	0.7735	0.6460	0.0234	0.8492	0.7417	1e-10	86
0.0185	0.7814	0.6534	0.0235	0.8490	0.7414	1e-10	87
0.0186	0.7762	0.6472	0.0234	0.8490	0.7415	1e-10	88
0.0189	0.7769	0.6481	0.0230	0.8514	0.7452	1e-10	89
0.0186	0.7776	0.6495	0.0238	0.8496	0.7422	1e-10	90
0.0188	0.7772	0.6486	0.0233	0.8496	0.7423	1e-10	91
0.0186	0.7743	0.6467	0.0231	0.8505	0.7436	1e-10	92
0.0188	0.7794	0.6505	0.0233	0.8503	0.7431	1e-10	93
0.0186	0.7739	0.6455	0.0237	0.8476	0.7395	1e-10	94
0.0188	0.7769	0.6477	0.0234	0.8492	0.7419	1e-10	95
0.0188	0.7689	0.6415	0.0236	0.8487	0.7409	1e-10	96
0.0194	0.7756	0.6476	0.0236	0.8504	0.7433	1e-10	97
0.0187	0.7792	0.6504	0.0231	0.8502	0.7436	1e-10	98
0.0186	0.7789	0.6508	0.0233	0.8506	0.7439	1e-10	99

Framework versions

Transformers 4.27.4
TensorFlow 2.10.1
Datasets 2.11.0
Tokenizers 0.13.3