mit-b1_segformer_ADE20k_RGB_IS_v1

Lit4pCol4b/mit-b1_segformer_ADE20k_RGB_IS_v1

mit-b1_segformer_ADE20k_RGB_IS_v1 is a machine learning model from Lit4pCol4b. 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: Jan 4, 2024
Updated: Jan 4, 2024
SHA: de900b47

Base models

nvidia/mit-b1

Model card

From the Hugging Face model README

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

Library: transformers
Type: segformer
License: other
Created: Jan 4, 2024
Updated: Jan 4, 2024