segment_test_1

wieheistdu/segment_test_1

segment_test_1 is a machine learning model from wieheistdu. 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.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: other
Model type: segformer
Access: Public
Created: Mar 12, 2023
Updated: Mar 12, 2023
SHA: c1c1a3f5

Model card

From the Hugging Face model README

segment_test_1

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1207
Mean Iou: 0.8483
Mean Accuracy: 0.9024
Overall Accuracy: 0.9649
Per Category Iou: [0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934]
Per Category Accuracy: [0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901]

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:

learning_rate: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
1.0371	0.12	20	1.0895	0.4995	0.6430	0.8631	[0.05521318178580855, 0.7743794151988705, 0.347543309879974, 0.8209439082840918]	[0.08660822249093107, 0.8923169863341743, 0.711708439278363, 0.8812568644974932]
0.6908	0.25	40	0.5755	0.5230	0.6242	0.8926	[0.023865281651135446, 0.8147455929204539, 0.3949463345707554, 0.858397589605038]	[0.02690009287979952, 0.9560483225800299, 0.618844995317415, 0.8951707417330758]
0.7532	0.38	60	0.4352	0.5425	0.6362	0.9073	[0.04830520945747081, 0.8390003744923147, 0.4011207931866636, 0.881716208435619]	[0.051771725987066926, 0.9667796831969788, 0.6132110672093029, 0.9128954438801464]
0.4797	0.5	80	0.3928	0.5754	0.6651	0.9187	[0.095160456358129, 0.8591208204996739, 0.4492600600843336, 0.8980719848369781]	[0.10522361249846661, 0.9658767606790423, 0.6592313641766187, 0.9299486923858831]
0.4974	0.62	100	0.3413	0.5757	0.6564	0.9231	[0.08465830140216431, 0.8655205765788001, 0.4482722747299048, 0.9043341709317356]	[0.08808466081348684, 0.9438250272055575, 0.6388503821434132, 0.9547541614780908]
0.6012	0.75	120	0.3019	0.6256	0.7003	0.9309	[0.20118898116264938, 0.8705267913237438, 0.5133711956715865, 0.9173562005725456]	[0.21758188318174648, 0.9550754656203344, 0.6747765579170281, 0.9537939455713355]
0.4656	0.88	140	0.2558	0.6196	0.6831	0.9354	[0.18448255342169326, 0.8799348242584422, 0.4896770706399499, 0.9242898593866574]	[0.18675411387413912, 0.957629264808883, 0.6245802352906291, 0.9633061744905276]
0.3084	1.0	160	0.2720	0.6122	0.6959	0.9306	[0.17521253603782794, 0.8710845346725691, 0.4824399357269581, 0.9201099745599581]	[0.18717908276816853, 0.9651212567854952, 0.6840404160211124, 0.9471146558242094]
0.3281	1.12	180	0.2593	0.6599	0.7300	0.9387	[0.3196354045076814, 0.8866606687882002, 0.5077873946739834, 0.9254476921045761]	[0.3419137619823704, 0.9647813859946743, 0.6571532354314773, 0.9563192436567626]
0.5177	1.25	200	0.2349	0.7152	0.7782	0.9445	[0.5227583225430057, 0.9035693700640127, 0.5129509983263923, 0.9217210470710874]	[0.5456644410563762, 0.9406031421756604, 0.6513240966564614, 0.9752501443811327]
0.3315	1.38	220	0.2277	0.6762	0.7332	0.9435	[0.3379172541018912, 0.8906873024131107, 0.5423203687425245, 0.93385833084482]	[0.3523035942729965, 0.9655347706627136, 0.650958385533654, 0.9641001079719775]
0.7584	1.5	240	0.2096	0.7006	0.7579	0.9411	[0.4065273757133468, 0.8956735509588918, 0.5836574037952176, 0.9166967934457825]	[0.4186732208261045, 0.9310246402349798, 0.702414928921078, 0.9793378276707719]
0.4114	1.62	260	0.2266	0.7157	0.7832	0.9389	[0.46836755859780127, 0.8939419126736278, 0.5910928241310868, 0.9094497536602162]	[0.5292527557261273, 0.9181066362487166, 0.7057063290263436, 0.9796145000506845]
0.2829	1.75	280	0.1932	0.7515	0.8111	0.9512	[0.5553894238891705, 0.9038650415073118, 0.6093098813145624, 0.9374419595745119]	[0.5760913376443579, 0.9716101928389251, 0.7359022068689445, 0.960860855647697]
0.2271	1.88	300	0.1864	0.7921	0.8488	0.9557	[0.72749813882911, 0.9128831336642784, 0.5892135224784095, 0.9387835557133554]	[0.7834695862467799, 0.9582075484693364, 0.682108077520874, 0.9715436644814973]
0.2779	2.0	320	0.1712	0.8180	0.8901	0.9587	[0.8078135832010197, 0.9212119018178146, 0.6029921836595201, 0.9400085237549578]	[0.8704261079859104, 0.9606214787743671, 0.7607928024097398, 0.9685803405441751]
0.4342	2.12	340	0.1708	0.7761	0.8320	0.9548	[0.6155968922409166, 0.9114185374153494, 0.6376315789473684, 0.939658585661896]	[0.640631845504092, 0.963783230780479, 0.7543977998028125, 0.9693606961542074]
0.3321	2.25	360	0.1498	0.8358	0.8902	0.9617	[0.8466817196643333, 0.9257548699292617, 0.6282106201058927, 0.9425208804748949]	[0.9010895851953105, 0.9624874568340255, 0.7247603727288228, 0.9726055284256458]
0.2023	2.38	380	0.1489	0.8266	0.8892	0.9595	[0.8245918770829803, 0.9218535979694774, 0.6204584829387099, 0.9396094918519875]	[0.8953634579324606, 0.9540908204687515, 0.7327368288397814, 0.9745457690515671]
0.1612	2.5	400	0.1474	0.8319	0.8833	0.9614	[0.8386504767201818, 0.9250884504372539, 0.6212118602838925, 0.9425717867382386]	[0.881909030369942, 0.9701526954959041, 0.7130847980310904, 0.9679183728130094]
0.2084	2.62	420	0.1456	0.8373	0.9156	0.9617	[0.8428293939804881, 0.9267780719611362, 0.6367325390207981, 0.9430187935498365]	[0.9257859733978234, 0.9552186457937082, 0.807640891445572, 0.9735885291166293]
0.164	2.75	440	0.1284	0.8345	0.8808	0.9617	[0.8596578459811758, 0.9255998505860551, 0.6105607176615363, 0.9421695191953943]	[0.9127433713614777, 0.9537728041909803, 0.6767583168392675, 0.9800493639325235]
0.1297	2.88	460	0.1385	0.8449	0.9054	0.9635	[0.8647724581323368, 0.9296026133121402, 0.6405698157260865, 0.9447385612148987]	[0.9357968385819182, 0.9633411073589047, 0.749796758514521, 0.9727959906219689]
0.1775	3.0	480	0.1483	0.8312	0.9131	0.9615	[0.8303105795421561, 0.9283098352535407, 0.6238329800678668, 0.9422278557852194]	[0.9462020048017104, 0.9651012565947649, 0.7745366217682133, 0.9665813244748572]
0.2044	3.12	500	0.1337	0.8371	0.8944	0.9626	[0.8455606368699817, 0.9275108345488315, 0.6310811541832109, 0.9440912228533584]	[0.9218363913569213, 0.9599956449915814, 0.7206757748504414, 0.9752020638364555]
0.1668	3.25	520	0.1329	0.8441	0.9025	0.9629	[0.8655859764078333, 0.9282151151180292, 0.6390094540302755, 0.9437038916825506]	[0.9213698017980128, 0.962867990264278, 0.7534242174893931, 0.9724944874772036]
0.1279	3.38	540	0.1288	0.8347	0.8822	0.9624	[0.8622657989592432, 0.9265309615800243, 0.6059068424468966, 0.9439998815395132]	[0.9212317964355187, 0.9664261698919513, 0.6689078828044113, 0.9723242060897683]
0.2441	3.5	560	0.1289	0.8456	0.9119	0.9632	[0.8699084528348765, 0.9296467606481729, 0.6388408116225611, 0.9439747399023978]	[0.9510540981020977, 0.9645672117667877, 0.7615761160984553, 0.9703859184453523]
0.261	3.62	580	0.1373	0.8408	0.9059	0.9626	[0.8702262323466258, 0.9285138712580321, 0.6209427648568083, 0.9435318648195579]	[0.9490453533813504, 0.9671653822394067, 0.7391515996155091, 0.9684196380660275]
0.5753	3.75	600	0.1251	0.8458	0.8983	0.9627	[0.8760502510695999, 0.9269758886426209, 0.6368816580927279, 0.9433603638703384]	[0.9298275590137217, 0.9542444643180703, 0.7302238003810314, 0.9788558132354487]
0.1992	3.88	620	0.1289	0.8480	0.9126	0.9636	[0.8658569991741146, 0.9291059762753285, 0.6517419325899511, 0.9451333803283866]	[0.9278188142929744, 0.9559565336252214, 0.7898717787151185, 0.9769391943858292]
0.2911	4.0	640	0.1263	0.8451	0.8990	0.9623	[0.8676994568324171, 0.9257222374094188, 0.6441978583675714, 0.9428325001046762]	[0.9276851900530992, 0.9481842740751965, 0.7380742344158877, 0.9821508650313081]
0.2322	4.12	660	0.1234	0.8448	0.8923	0.9638	[0.8776973476081853, 0.929274732930499, 0.626829659547631, 0.9455449914778657]	[0.9237794192383857, 0.968169497775343, 0.7048637108312268, 0.972200331262863]
0.2316	4.25	680	0.1228	0.8493	0.9069	0.9642	[0.8763959224746586, 0.9306747334769754, 0.64476604812217, 0.9454498311627832]	[0.9386358060389394, 0.9577756238205185, 0.753671319599398, 0.9776486846669562]
0.2397	4.38	700	0.1262	0.8442	0.8963	0.9640	[0.8718388103686404, 0.9303958268346455, 0.6286213954621487, 0.94588759466782]	[0.9306095894011882, 0.9690709633257429, 0.7141201558720109, 0.971342786383813]
0.2163	4.5	720	0.1273	0.8438	0.8975	0.9635	[0.8711635054714977, 0.9292374082869599, 0.6295386570878305, 0.945216509372582]	[0.9251331861276133, 0.9722796031962159, 0.724520683682118, 0.9680362026971605]
0.1596	4.62	740	0.1207	0.8515	0.9108	0.9646	[0.8777356406779111, 0.9314006904528226, 0.6507271627753556, 0.9461335840246559]	[0.9470848010094106, 0.9579392015394054, 0.7604592145612331, 0.9777115520716019]
0.2282	4.75	760	0.1192	0.8504	0.9024	0.9650	[0.8835435013923162, 0.9318821516960537, 0.6391197674041569, 0.947006215721923]	[0.9404013984543399, 0.9611932723067028, 0.7305820984405386, 0.9775071400073841]
0.1647	4.88	780	0.1197	0.8518	0.9100	0.9647	[0.8779367174977694, 0.9313115604274004, 0.6518294433470307, 0.9462412590163557]	[0.9526751134710758, 0.9568937611060662, 0.7517933435633607, 0.9786714889809993]
0.1087	5.0	800	0.1207	0.8483	0.9024	0.9649	[0.8797073511948335, 0.9320756407098275, 0.6346731005607217, 0.946842856578934]	[0.9476937770534322, 0.9647476108381431, 0.722163329552671, 0.9748104445450901]

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cpu
Datasets 2.10.1
Tokenizers 0.13.2

Library: transformers
Type: segformer
License: other
Created: Mar 12, 2023
Updated: Mar 12, 2023