Lit4pCol4b/mit-b1_segformer_ADE20k_RGB_IS_v1

This model is a fine-tuned version of nvidia/mit-b1 on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0787
• Validation Loss: 0.1007
• Validation Mean Iou: 0.7646
• Validation Mean Accuracy: 0.8701
• Validation Overall Accuracy: 0.9687
• Validation Accuracy Unlabeled: 0.6791
• Validation Accuracy Objeto Interes: 0.9475
• Validation Accuracy Agua: 0.9838
• Validation Iou Unlabeled: 0.5173
• Validation Iou Objeto Interes: 0.8005
• Validation Iou Agua: 0.9760
• Epoch: 39

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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Validation Mean Iou	Validation Mean Accuracy	Validation Overall Accuracy	Validation Accuracy Unlabeled	Validation Accuracy Objeto Interes	Validation Accuracy Agua	Validation Iou Unlabeled	Validation Iou Objeto Interes	Validation Iou Agua	Epoch
0.6580	0.6718	0.4196	0.6281	0.8767	0.0254	0.9466	0.9124	0.0232	0.3345	0.9012	0
0.4551	0.5040	0.5131	0.6832	0.9126	0.1813	0.9216	0.9467	0.1234	0.4837	0.9322	1
0.3472	0.2565	0.5381	0.6560	0.9375	0.1035	0.8839	0.9805	0.0930	0.5671	0.9542	2
0.2846	0.2434	0.6188	0.7343	0.9442	0.3415	0.8847	0.9767	0.2514	0.6486	0.9564	3
0.2383	0.2245	0.6401	0.7568	0.9469	0.4203	0.8735	0.9767	0.2975	0.6644	0.9586	4
0.2075	0.2243	0.6606	0.7809	0.9501	0.4690	0.8975	0.9764	0.3332	0.6879	0.9608	5
0.1943	0.1820	0.6721	0.7704	0.9559	0.4301	0.8964	0.9847	0.3423	0.7083	0.9658	6
0.1835	0.2237	0.6866	0.8243	0.9510	0.5945	0.9077	0.9707	0.3844	0.7151	0.9601	7
0.1645	0.1638	0.7110	0.8204	0.9586	0.6026	0.8779	0.9808	0.4292	0.7369	0.9670	8
0.1574	0.1359	0.7140	0.8058	0.9616	0.5380	0.8933	0.9861	0.4197	0.7527	0.9695	9
0.1737	0.1421	0.7075	0.8042	0.9596	0.5320	0.8965	0.9841	0.4037	0.7513	0.9675	10
0.1608	0.1613	0.7046	0.8348	0.9564	0.6010	0.9285	0.9750	0.4156	0.7325	0.9655	11
0.1425	0.1387	0.7268	0.8355	0.9618	0.6140	0.9109	0.9816	0.4499	0.7605	0.9698	12
0.1299	0.1230	0.7198	0.8184	0.9628	0.5475	0.9225	0.9851	0.4286	0.7595	0.9714	13
0.1286	0.1279	0.7267	0.8320	0.9630	0.5856	0.9270	0.9833	0.4473	0.7614	0.9715	14
0.1322	0.1201	0.7428	0.8380	0.9651	0.6334	0.8954	0.9854	0.4772	0.7791	0.9722	15
0.1203	0.1076	0.7439	0.8294	0.9663	0.6001	0.9000	0.9880	0.4712	0.7872	0.9732	16
0.1154	0.1314	0.7417	0.8557	0.9633	0.6671	0.9198	0.9802	0.4752	0.7794	0.9706	17
0.1145	0.1098	0.7446	0.8438	0.9662	0.6183	0.9281	0.9852	0.4827	0.7770	0.9739	18
0.1131	0.0994	0.7500	0.8368	0.9676	0.6077	0.9145	0.9881	0.4834	0.7919	0.9748	19
0.1101	0.1157	0.7590	0.8657	0.9664	0.7130	0.9015	0.9827	0.5107	0.7928	0.9733	20
0.1045	0.1099	0.7513	0.8565	0.9664	0.6570	0.9288	0.9835	0.4959	0.7841	0.9739	21
0.1031	0.1045	0.7511	0.8522	0.9668	0.6398	0.9323	0.9846	0.4911	0.7878	0.9743	22
0.1038	0.1245	0.7335	0.8535	0.9628	0.6322	0.9488	0.9794	0.4609	0.7683	0.9713	23
0.0989	0.1130	0.7476	0.8608	0.9652	0.6641	0.9372	0.9813	0.4895	0.7805	0.9729	24
0.0961	0.0993	0.7534	0.8560	0.9672	0.6481	0.9356	0.9844	0.4949	0.7904	0.9748	25
0.0931	0.0977	0.7616	0.8574	0.9684	0.6623	0.9242	0.9858	0.5099	0.7995	0.9754	26
0.0913	0.0899	0.7685	0.8547	0.9701	0.6575	0.9184	0.9883	0.5192	0.8096	0.9768	27
0.0899	0.0984	0.7572	0.8550	0.9683	0.6393	0.9398	0.9858	0.5015	0.7940	0.9759	28
0.0918	0.1307	0.7440	0.8719	0.9635	0.6838	0.9545	0.9773	0.4872	0.7735	0.9713	29
0.0919	0.1239	0.7405	0.8590	0.9641	0.6442	0.9526	0.9801	0.4707	0.7784	0.9725	30
0.0925	0.0990	0.7699	0.8629	0.9696	0.6859	0.9163	0.9865	0.5271	0.8067	0.9761	31
0.0889	0.1069	0.7563	0.8708	0.9664	0.6864	0.9450	0.9811	0.5038	0.7913	0.9738	32
0.0836	0.0913	0.7707	0.8617	0.9702	0.6714	0.9265	0.9873	0.5265	0.8086	0.9770	33
0.0822	0.1041	0.7645	0.8788	0.9672	0.7170	0.9383	0.9809	0.5161	0.8035	0.9740	34
0.0803	0.0981	0.7699	0.8721	0.9691	0.6987	0.9334	0.9843	0.5291	0.8046	0.9759	35
0.0800	0.1018	0.7597	0.8681	0.9678	0.6728	0.9485	0.9830	0.5104	0.7935	0.9752	36
0.0779	0.0975	0.7727	0.8769	0.9692	0.7185	0.9286	0.9837	0.5349	0.8075	0.9757	37
0.0756	0.0984	0.7697	0.8742	0.9691	0.7003	0.9385	0.9838	0.5280	0.8051	0.9760	38
0.0787	0.1007	0.7646	0.8701	0.9687	0.6791	0.9475	0.9838	0.5173	0.8005	0.9760	39

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.0