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AhamadShaik/
SegFormer_RESIZE_LM

transformers

segformer

generated_from_keras_callback

other

endpoints_compatible

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AhamadShaik/SegFormer_RESIZE_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: 0.0520
Train Dice Coef: 0.8435
Train Iou: 0.7409
Validation Loss: 0.0454
Validation Dice Coef: 0.8836
Validation Iou: 0.7926
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
0.3496	0.3697	0.2435	0.2697	0.1141	0.0635	1e-04	0
0.1591	0.5600	0.4126	0.1768	0.3601	0.2345	1e-04	1
0.1295	0.6470	0.5014	0.1637	0.4628	0.3163	1e-04	2
0.1109	0.6903	0.5511	0.1319	0.5634	0.4072	1e-04	3
0.1018	0.7226	0.5858	0.0932	0.7480	0.6051	1e-04	4
0.0930	0.7373	0.6042	0.1618	0.5048	0.3614	1e-04	5
0.0878	0.7534	0.6255	0.1023	0.7076	0.5637	1e-04	6
0.0842	0.7585	0.6310	0.0878	0.7726	0.6384	1e-04	7
0.0798	0.7733	0.6475	0.0966	0.7434	0.5996	1e-04	8
0.0765	0.7716	0.6487	0.1073	0.7157	0.5657	1e-04	9
0.0701	0.7974	0.6794	0.1049	0.7190	0.5811	1e-04	10
0.0675	0.8020	0.6854	0.1319	0.6935	0.5427	1e-04	11
0.0662	0.8108	0.6957	0.1593	0.6269	0.4826	1e-04	12
0.0679	0.7980	0.6817	0.0483	0.8809	0.7881	5e-06	13
0.0613	0.8173	0.7069	0.0467	0.8827	0.7910	5e-06	14
0.0595	0.8160	0.7064	0.0475	0.8810	0.7883	5e-06	15
0.0589	0.8197	0.7115	0.0460	0.8835	0.7922	5e-06	16
0.0576	0.8214	0.7134	0.0459	0.8838	0.7927	5e-06	17
0.0577	0.8161	0.7082	0.0458	0.8838	0.7927	5e-06	18
0.0564	0.8310	0.7242	0.0461	0.8830	0.7915	5e-06	19
0.0567	0.8330	0.7271	0.0455	0.8828	0.7910	5e-06	20
0.0562	0.8252	0.7182	0.0455	0.8850	0.7947	5e-06	21
0.0560	0.8245	0.7188	0.0461	0.8830	0.7916	5e-06	22
0.0554	0.8259	0.7208	0.0463	0.8811	0.7885	5e-06	23
0.0548	0.8254	0.7212	0.0459	0.8832	0.7919	5e-06	24
0.0552	0.8331	0.7281	0.0452	0.8833	0.7920	5e-06	25
0.0534	0.8391	0.7355	0.0438	0.8872	0.7982	5e-06	26
0.0538	0.8350	0.7310	0.0447	0.8846	0.7941	5e-06	27
0.0543	0.8443	0.7406	0.0468	0.8803	0.7877	5e-06	28
0.0535	0.8350	0.7324	0.0459	0.8833	0.7919	5e-06	29
0.0529	0.8404	0.7376	0.0460	0.8820	0.7900	5e-06	30
0.0525	0.8396	0.7379	0.0444	0.8855	0.7954	5e-06	31
0.0525	0.8347	0.7322	0.0458	0.8833	0.7921	2.5e-07	32
0.0524	0.8414	0.7376	0.0453	0.8840	0.7930	2.5e-07	33
0.0524	0.8406	0.7372	0.0446	0.8842	0.7935	2.5e-07	34
0.0522	0.8408	0.7385	0.0456	0.8838	0.7927	2.5e-07	35
0.0521	0.8484	0.7454	0.0453	0.8839	0.7929	2.5e-07	36
0.0521	0.8503	0.7481	0.0459	0.8832	0.7919	1.25e-08	37
0.0521	0.8370	0.7344	0.0451	0.8845	0.7939	1.25e-08	38
0.0524	0.8484	0.7452	0.0456	0.8837	0.7927	1.25e-08	39
0.0529	0.8410	0.7388	0.0448	0.8848	0.7944	1.25e-08	40
0.0519	0.8402	0.7391	0.0444	0.8852	0.7951	1.25e-08	41
0.0518	0.8349	0.7331	0.0448	0.8850	0.7948	6.25e-10	42
0.0523	0.8406	0.7381	0.0452	0.8835	0.7922	6.25e-10	43
0.0519	0.8427	0.7402	0.0449	0.8854	0.7952	6.25e-10	44
0.0523	0.8445	0.7413	0.0453	0.8839	0.7930	6.25e-10	45
0.0519	0.8445	0.7434	0.0446	0.8858	0.7959	6.25e-10	46
0.0519	0.8388	0.7368	0.0447	0.8839	0.7929	1e-10	47
0.0518	0.8456	0.7438	0.0448	0.8847	0.7943	1e-10	48
0.0521	0.8447	0.7433	0.0442	0.8859	0.7961	1e-10	49
0.0520	0.8382	0.7359	0.0453	0.8838	0.7929	1e-10	50
0.0523	0.8469	0.7463	0.0448	0.8852	0.7951	1e-10	51
0.0515	0.8375	0.7362	0.0459	0.8825	0.7909	1e-10	52
0.0520	0.8447	0.7432	0.0443	0.8854	0.7954	1e-10	53
0.0523	0.8359	0.7337	0.0442	0.8860	0.7962	1e-10	54
0.0523	0.8352	0.7333	0.0440	0.8867	0.7974	1e-10	55
0.0523	0.8376	0.7347	0.0456	0.8846	0.7940	1e-10	56
0.0520	0.8466	0.7441	0.0448	0.8856	0.7956	1e-10	57
0.0524	0.8382	0.7357	0.0433	0.8875	0.7987	1e-10	58
0.0521	0.8431	0.7403	0.0450	0.8853	0.7951	1e-10	59
0.0524	0.8415	0.7389	0.0453	0.8846	0.7940	1e-10	60
0.0517	0.8436	0.7423	0.0444	0.8853	0.7951	1e-10	61
0.0523	0.8467	0.7443	0.0455	0.8840	0.7932	1e-10	62
0.0522	0.8470	0.7434	0.0445	0.8859	0.7961	1e-10	63
0.0520	0.8375	0.7356	0.0446	0.8857	0.7958	1e-10	64
0.0515	0.8416	0.7396	0.0440	0.8862	0.7966	1e-10	65
0.0526	0.8364	0.7346	0.0449	0.8848	0.7944	1e-10	66
0.0524	0.8461	0.7438	0.0452	0.8838	0.7928	1e-10	67
0.0523	0.8374	0.7361	0.0453	0.8849	0.7947	1e-10	68
0.0520	0.8370	0.7355	0.0446	0.8852	0.7950	1e-10	69
0.0522	0.8487	0.7473	0.0455	0.8835	0.7923	1e-10	70
0.0520	0.8446	0.7429	0.0463	0.8828	0.7911	1e-10	71
0.0520	0.8364	0.7345	0.0454	0.8841	0.7933	1e-10	72
0.0528	0.8468	0.7431	0.0452	0.8846	0.7939	1e-10	73
0.0518	0.8455	0.7441	0.0449	0.8846	0.7940	1e-10	74
0.0519	0.8351	0.7330	0.0445	0.8852	0.7948	1e-10	75
0.0521	0.8423	0.7406	0.0453	0.8849	0.7945	1e-10	76
0.0525	0.8467	0.7449	0.0456	0.8836	0.7925	1e-10	77
0.0522	0.8436	0.7397	0.0445	0.8847	0.7942	1e-10	78
0.0521	0.8423	0.7393	0.0443	0.8855	0.7954	1e-10	79
0.0513	0.8439	0.7415	0.0454	0.8837	0.7926	1e-10	80
0.0520	0.8433	0.7422	0.0445	0.8843	0.7937	1e-10	81
0.0522	0.8417	0.7396	0.0451	0.8844	0.7939	1e-10	82
0.0520	0.8492	0.7471	0.0449	0.8847	0.7943	1e-10	83
0.0526	0.8384	0.7360	0.0445	0.8862	0.7968	1e-10	84
0.0520	0.8477	0.7457	0.0447	0.8844	0.7938	1e-10	85
0.0518	0.8410	0.7387	0.0452	0.8848	0.7944	1e-10	86
0.0523	0.8443	0.7421	0.0443	0.8865	0.7971	1e-10	87
0.0519	0.8429	0.7402	0.0465	0.8813	0.7891	1e-10	88
0.0526	0.8328	0.7291	0.0446	0.8853	0.7952	1e-10	89
0.0528	0.8435	0.7408	0.0449	0.8855	0.7954	1e-10	90
0.0521	0.8417	0.7399	0.0441	0.8859	0.7961	1e-10	91
0.0516	0.8430	0.7422	0.0455	0.8830	0.7915	1e-10	92
0.0523	0.8499	0.7491	0.0457	0.8832	0.7919	1e-10	93
0.0525	0.8399	0.7371	0.0452	0.8846	0.7940	1e-10	94
0.0517	0.8433	0.7401	0.0455	0.8835	0.7924	1e-10	95
0.0524	0.8417	0.7398	0.0453	0.8831	0.7917	1e-10	96
0.0517	0.8410	0.7390	0.0440	0.8870	0.7979	1e-10	97
0.0525	0.8394	0.7373	0.0451	0.8846	0.7941	1e-10	98
0.0520	0.8435	0.7409	0.0454	0.8836	0.7926	1e-10	99

Framework versions

Transformers 4.27.4
TensorFlow 2.10.1
Datasets 2.11.0
Tokenizers 0.13.3