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segformer-b5-finetuned-segments-instryde-foot-test

Name: segformer-b5-finetuned-segments-instryde-foot-test
Availability: InStock

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segformer-b5-finetuned-segments-instryde-foot-test

This model is a fine-tuned version of nvidia/mit-b5 on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set:

Loss: 0.0496
Mean Iou: 0.4672
Mean Accuracy: 0.9344
Overall Accuracy: 0.9344
Per Category Iou: [0.0, 0.9343870058298716]
Per Category Accuracy: [nan, 0.9343870058298716]

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.1392	0.23	20	0.2371	0.4064	0.8128	0.8128	[0.0, 0.8127920708469037]	[nan, 0.8127920708469037]
0.2273	0.45	40	0.0993	0.4449	0.8898	0.8898	[0.0, 0.889800913515142]	[nan, 0.889800913515142]
0.0287	0.68	60	0.0607	0.4190	0.8379	0.8379	[0.0, 0.8379005425233161]	[nan, 0.8379005425233161]
0.03	0.91	80	0.0572	0.4072	0.8144	0.8144	[0.0, 0.8144304164916533]	[nan, 0.8144304164916533]
0.0239	1.14	100	0.0577	0.3973	0.7946	0.7946	[0.0, 0.7946284254068925]	[nan, 0.7946284254068925]
0.0196	1.36	120	0.0425	0.4227	0.8455	0.8455	[0.0, 0.8454754171184029]	[nan, 0.8454754171184029]
0.0295	1.59	140	0.0368	0.4479	0.8958	0.8958	[0.0, 0.895802316554768]	[nan, 0.895802316554768]
0.0297	1.82	160	0.0441	0.4561	0.9121	0.9121	[0.0, 0.9121241975954804]	[nan, 0.9121241975954804]
0.0276	2.05	180	0.0332	0.4629	0.9258	0.9258	[0.0, 0.925774145806165]	[nan, 0.925774145806165]
0.0148	2.27	200	0.0395	0.4310	0.8621	0.8621	[0.0, 0.8620666905637888]	[nan, 0.8620666905637888]
0.012	2.5	220	0.0372	0.4381	0.8761	0.8761	[0.0, 0.8761025846276997]	[nan, 0.8761025846276997]
0.0117	2.73	240	0.0339	0.4471	0.8941	0.8941	[0.0, 0.8941320836457919]	[nan, 0.8941320836457919]
0.0198	2.95	260	0.0297	0.4485	0.8969	0.8969	[0.0, 0.8969491585060927]	[nan, 0.8969491585060927]
0.0247	3.18	280	0.0303	0.4565	0.9130	0.9130	[0.0, 0.9130423308930413]	[nan, 0.9130423308930413]
0.0115	3.41	300	0.0307	0.4533	0.9066	0.9066	[0.0, 0.9065626188900153]	[nan, 0.9065626188900153]
0.0164	3.64	320	0.0330	0.4549	0.9097	0.9097	[0.0, 0.9097436483868343]	[nan, 0.9097436483868343]
0.0114	3.86	340	0.0362	0.4425	0.8850	0.8850	[0.0, 0.8849727418868903]	[nan, 0.8849727418868903]
0.012	4.09	360	0.0321	0.4582	0.9164	0.9164	[0.0, 0.9164498699219532]	[nan, 0.9164498699219532]
0.0153	4.32	380	0.0321	0.4572	0.9144	0.9144	[0.0, 0.9144310762281544]	[nan, 0.9144310762281544]
0.0115	4.55	400	0.0307	0.4573	0.9145	0.9145	[0.0, 0.9145300367033407]	[nan, 0.9145300367033407]
0.0139	4.77	420	0.0330	0.4678	0.9357	0.9357	[0.0, 0.935664695520609]	[nan, 0.935664695520609]
0.014	5.0	440	0.0317	0.4635	0.9271	0.9271	[0.0, 0.9270562337402442]	[nan, 0.9270562337402442]
0.0197	5.23	460	0.0320	0.4678	0.9356	0.9356	[0.0, 0.9355745315321061]	[nan, 0.9355745315321061]
0.0086	5.45	480	0.0337	0.4607	0.9214	0.9214	[0.0, 0.9213528116870122]	[nan, 0.9213528116870122]
0.3103	5.68	500	0.0338	0.4548	0.9096	0.9096	[0.0, 0.9095853116265363]	[nan, 0.9095853116265363]
0.0088	5.91	520	0.0305	0.4635	0.9270	0.9270	[0.0, 0.9270243464760175]	[nan, 0.9270243464760175]
0.0119	6.14	540	0.0299	0.4680	0.9359	0.9359	[0.0, 0.9359494817769782]	[nan, 0.9359494817769782]
0.0114	6.36	560	0.0314	0.4574	0.9148	0.9148	[0.0, 0.914796130425508]	[nan, 0.914796130425508]
0.0122	6.59	580	0.0289	0.4613	0.9227	0.9227	[0.0, 0.9226920767845322]	[nan, 0.9226920767845322]
0.0164	6.82	600	0.0312	0.4620	0.9240	0.9240	[0.0, 0.9239807620836238]	[nan, 0.9239807620836238]
0.0062	7.05	620	0.0335	0.4605	0.9210	0.9210	[0.0, 0.9209954544155065]	[nan, 0.9209954544155065]
0.0089	7.27	640	0.0309	0.4659	0.9317	0.9317	[0.0, 0.9317029778306545]	[nan, 0.9317029778306545]
0.0251	7.5	660	0.0291	0.4734	0.9468	0.9468	[0.0, 0.9467878529315391]	[nan, 0.9467878529315391]
0.0065	7.73	680	0.0326	0.4598	0.9195	0.9195	[0.0, 0.9195297398219151]	[nan, 0.9195297398219151]
0.0056	7.95	700	0.0310	0.4606	0.9213	0.9213	[0.0, 0.9212714441851925]	[nan, 0.9212714441851925]
0.0099	8.18	720	0.0345	0.4503	0.9006	0.9006	[0.0, 0.9006183930138303]	[nan, 0.9006183930138303]
0.0103	8.41	740	0.0335	0.4539	0.9078	0.9078	[0.0, 0.9077512441530853]	[nan, 0.9077512441530853]
0.0065	8.64	760	0.0334	0.4544	0.9088	0.9088	[0.0, 0.9087936278250467]	[nan, 0.9087936278250467]
0.0047	8.86	780	0.0341	0.4557	0.9114	0.9114	[0.0, 0.9114215782216583]	[nan, 0.9114215782216583]
0.0105	9.09	800	0.0315	0.4597	0.9195	0.9195	[0.0, 0.9194703635368034]	[nan, 0.9194703635368034]
0.0087	9.32	820	0.0329	0.4583	0.9166	0.9166	[0.0, 0.9165708216138474]	[nan, 0.9165708216138474]
0.0122	9.55	840	0.0357	0.4537	0.9073	0.9073	[0.0, 0.9073004242105703]	[nan, 0.9073004242105703]
0.0057	9.77	860	0.0319	0.4621	0.9241	0.9241	[0.0, 0.9241050124580242]	[nan, 0.9241050124580242]
0.0068	10.0	880	0.0342	0.4539	0.9078	0.9078	[0.0, 0.907799624829843]	[nan, 0.907799624829843]
0.0095	10.23	900	0.0340	0.4578	0.9156	0.9156	[0.0, 0.9155933120311748]	[nan, 0.9155933120311748]
0.0043	10.45	920	0.0319	0.4636	0.9272	0.9272	[0.0, 0.9271771854321385]	[nan, 0.9271771854321385]
0.0049	10.68	940	0.0308	0.4659	0.9319	0.9319	[0.0, 0.9318525181042692]	[nan, 0.9318525181042692]
0.005	10.91	960	0.0319	0.4640	0.9281	0.9281	[0.0, 0.9280612323438019]	[nan, 0.9280612323438019]
0.0043	11.14	980	0.0313	0.4653	0.9306	0.9306	[0.0, 0.930638602941985]	[nan, 0.930638602941985]
0.0084	11.36	1000	0.0321	0.4632	0.9264	0.9264	[0.0, 0.9264294840640648]	[nan, 0.9264294840640648]
0.0044	11.59	1020	0.0320	0.4643	0.9285	0.9285	[0.0, 0.9285241474555063]	[nan, 0.9285241474555063]
0.0044	11.82	1040	0.0321	0.4661	0.9321	0.9321	[0.0, 0.9321098153397533]	[nan, 0.9321098153397533]
0.0057	12.05	1060	0.0338	0.4626	0.9253	0.9253	[0.0, 0.9252518544093489]	[nan, 0.9252518544093489]
0.0064	12.27	1080	0.0348	0.4616	0.9231	0.9231	[0.0, 0.9231450958487181]	[nan, 0.9231450958487181]
0.0075	12.5	1100	0.0331	0.4618	0.9237	0.9237	[0.0, 0.9236706859280404]	[nan, 0.9236706859280404]
0.0103	12.73	1120	0.0317	0.4704	0.9408	0.9408	[0.0, 0.9408425274945187]	[nan, 0.9408425274945187]
0.0053	12.95	1140	0.0320	0.4704	0.9407	0.9407	[0.0, 0.9407292727284723]	[nan, 0.9407292727284723]
0.0073	13.18	1160	0.0331	0.4652	0.9305	0.9305	[0.0, 0.9304681710124976]	[nan, 0.9304681710124976]
0.0052	13.41	1180	0.0342	0.4664	0.9328	0.9328	[0.0, 0.9328047377877275]	[nan, 0.9328047377877275]
0.0089	13.64	1200	0.0322	0.4676	0.9353	0.9353	[0.0, 0.9352996413232555]	[nan, 0.9352996413232555]
0.0054	13.86	1220	0.0332	0.4655	0.9311	0.9311	[0.0, 0.9310509382552609]	[nan, 0.9310509382552609]
0.0057	14.09	1240	0.0333	0.4661	0.9321	0.9321	[0.0, 0.9321439017256508]	[nan, 0.9321439017256508]
0.0047	14.32	1260	0.0346	0.4639	0.9278	0.9278	[0.0, 0.9277522557490538]	[nan, 0.9277522557490538]
0.0092	14.55	1280	0.0380	0.4583	0.9166	0.9166	[0.0, 0.9166290983381238]	[nan, 0.9166290983381238]
0.0066	14.77	1300	0.0338	0.4638	0.9277	0.9277	[0.0, 0.927687381659765]	[nan, 0.927687381659765]
0.0076	15.0	1320	0.0347	0.4640	0.9280	0.9280	[0.0, 0.9279897608895007]	[nan, 0.9279897608895007]
0.0054	15.23	1340	0.0345	0.4647	0.9295	0.9295	[0.0, 0.9294664710914461]	[nan, 0.9294664710914461]
0.0036	15.45	1360	0.0349	0.4666	0.9332	0.9332	[0.0, 0.9331950818842955]	[nan, 0.9331950818842955]
0.004	15.68	1380	0.0352	0.4617	0.9234	0.9234	[0.0, 0.9234408777134413]	[nan, 0.9234408777134413]
0.0042	15.91	1400	0.0357	0.4622	0.9244	0.9244	[0.0, 0.9244282833436326]	[nan, 0.9244282833436326]
0.0048	16.14	1420	0.0370	0.4586	0.9172	0.9172	[0.0, 0.9171546884174461]	[nan, 0.9171546884174461]
0.0043	16.36	1440	0.0345	0.4647	0.9294	0.9294	[0.0, 0.9294411811922318]	[nan, 0.9294411811922318]
0.0027	16.59	1460	0.0354	0.4667	0.9334	0.9334	[0.0, 0.9333754098613014]	[nan, 0.9333754098613014]
0.0057	16.82	1480	0.0364	0.4689	0.9379	0.9379	[0.0, 0.9378913062122988]	[nan, 0.9378913062122988]
0.0035	17.05	1500	0.0363	0.4662	0.9325	0.9325	[0.0, 0.9324682721720945]	[nan, 0.9324682721720945]
0.0029	17.27	1520	0.0348	0.4674	0.9347	0.9347	[0.0, 0.9347212723238338]	[nan, 0.9347212723238338]
0.0043	17.5	1540	0.0362	0.4648	0.9295	0.9295	[0.0, 0.9295390421065827]	[nan, 0.9295390421065827]
0.0041	17.73	1560	0.0347	0.4664	0.9328	0.9328	[0.0, 0.9328487202211436]	[nan, 0.9328487202211436]
0.003	17.95	1580	0.0364	0.4649	0.9297	0.9297	[0.0, 0.9297237683269303]	[nan, 0.9297237683269303]
0.0121	18.18	1600	0.0364	0.4650	0.9300	0.9300	[0.0, 0.9299920611707684]	[nan, 0.9299920611707684]
0.004	18.41	1620	0.0369	0.4667	0.9334	0.9334	[0.0, 0.9334259896597299]	[nan, 0.9334259896597299]
0.0035	18.64	1640	0.0368	0.4636	0.9272	0.9272	[0.0, 0.9272475573256042]	[nan, 0.9272475573256042]
0.0031	18.86	1660	0.0358	0.4665	0.9330	0.9330	[0.0, 0.9329784683997212]	[nan, 0.9329784683997212]
0.0032	19.09	1680	0.0357	0.4661	0.9322	0.9322	[0.0, 0.9321515986514985]	[nan, 0.9321515986514985]
0.0047	19.32	1700	0.0371	0.4621	0.9243	0.9243	[0.0, 0.9242886391175364]	[nan, 0.9242886391175364]
0.0056	19.55	1720	0.0359	0.4663	0.9326	0.9326	[0.0, 0.9326277084932278]	[nan, 0.9326277084932278]
0.0033	19.77	1740	0.0348	0.4694	0.9389	0.9389	[0.0, 0.9388523223824404]	[nan, 0.9388523223824404]
0.0049	20.0	1760	0.0394	0.4612	0.9224	0.9224	[0.0, 0.9223918966764674]	[nan, 0.9223918966764674]
0.0058	20.23	1780	0.0368	0.4660	0.9321	0.9321	[0.0, 0.9320724302713497]	[nan, 0.9320724302713497]
0.003	20.45	1800	0.0370	0.4686	0.9372	0.9372	[0.0, 0.9371787907909581]	[nan, 0.9371787907909581]
0.0058	20.68	1820	0.0363	0.4665	0.9330	0.9330	[0.0, 0.9329949618122522]	[nan, 0.9329949618122522]
0.0083	20.91	1840	0.0351	0.4661	0.9322	0.9322	[0.0, 0.9321834859157253]	[nan, 0.9321834859157253]
0.0036	21.14	1860	0.0353	0.4667	0.9333	0.9333	[0.0, 0.9333149340153543]	[nan, 0.9333149340153543]
0.0032	21.36	1880	0.0373	0.4657	0.9314	0.9314	[0.0, 0.93137640826254]	[nan, 0.93137640826254]
0.005	21.59	1900	0.0391	0.4647	0.9294	0.9294	[0.0, 0.929370809298766]	[nan, 0.929370809298766]
0.0049	21.82	1920	0.0364	0.4701	0.9403	0.9403	[0.0, 0.9402795523467927]	[nan, 0.9402795523467927]
0.0044	22.05	1940	0.0368	0.4672	0.9343	0.9343	[0.0, 0.9343111361322288]	[nan, 0.9343111361322288]
0.0038	22.27	1960	0.0367	0.4663	0.9325	0.9325	[0.0, 0.932513354166346]	[nan, 0.932513354166346]
0.0032	22.5	1980	0.0378	0.4679	0.9358	0.9358	[0.0, 0.9358483221801213]	[nan, 0.9358483221801213]
0.0039	22.73	2000	0.0381	0.4653	0.9306	0.9306	[0.0, 0.9305517376359882]	[nan, 0.9305517376359882]
0.0032	22.95	2020	0.0385	0.4651	0.9301	0.9301	[0.0, 0.9301262075926875]	[nan, 0.9301262075926875]
0.0058	23.18	2040	0.0381	0.4654	0.9309	0.9309	[0.0, 0.9308673115957486]	[nan, 0.9308673115957486]
0.0049	23.41	2060	0.0377	0.4658	0.9316	0.9316	[0.0, 0.9316194112071639]	[nan, 0.9316194112071639]
0.0032	23.64	2080	0.0373	0.4692	0.9384	0.9384	[0.0, 0.9384256927783043]	[nan, 0.9384256927783043]
0.0056	23.86	2100	0.0390	0.4646	0.9292	0.9292	[0.0, 0.9292465589243656]	[nan, 0.9292465589243656]
0.003	24.09	2120	0.0383	0.4658	0.9317	0.9317	[0.0, 0.9316765883706047]	[nan, 0.9316765883706047]
0.0037	24.32	2140	0.0376	0.4668	0.9337	0.9337	[0.0, 0.9336755899693663]	[nan, 0.9336755899693663]
0.0025	24.55	2160	0.0390	0.4663	0.9326	0.9326	[0.0, 0.9326145137632029]	[nan, 0.9326145137632029]
0.0039	24.77	2180	0.0381	0.4688	0.9376	0.9376	[0.0, 0.937613117320942]	[nan, 0.937613117320942]
0.0031	25.0	2200	0.0395	0.4645	0.9291	0.9291	[0.0, 0.9290629322648534]	[nan, 0.9290629322648534]
0.0026	25.23	2220	0.0389	0.4668	0.9336	0.9336	[0.0, 0.9335678330074968]	[nan, 0.9335678330074968]
0.0028	25.45	2240	0.0375	0.4680	0.9359	0.9359	[0.0, 0.9359329883644473]	[nan, 0.9359329883644473]
0.0039	25.68	2260	0.0404	0.4656	0.9312	0.9312	[0.0, 0.9312004785288756]	[nan, 0.9312004785288756]
0.004	25.91	2280	0.0371	0.4716	0.9431	0.9431	[0.0, 0.9431021250112706]	[nan, 0.9431021250112706]
0.0048	26.14	2300	0.0373	0.4700	0.9400	0.9400	[0.0, 0.9399639783870323]	[nan, 0.9399639783870323]
0.0033	26.36	2320	0.0385	0.4688	0.9377	0.9377	[0.0, 0.9376560001935227]	[nan, 0.9376560001935227]
0.0042	26.59	2340	0.0374	0.4686	0.9372	0.9372	[0.0, 0.9371743925476165]	[nan, 0.9371743925476165]
0.0048	26.82	2360	0.0393	0.4660	0.9320	0.9320	[0.0, 0.9319789676003404]	[nan, 0.9319789676003404]
0.0047	27.05	2380	0.0393	0.4650	0.9300	0.9300	[0.0, 0.9300162515091472]	[nan, 0.9300162515091472]
0.0048	27.27	2400	0.0389	0.4670	0.9340	0.9340	[0.0, 0.9339867656857851]	[nan, 0.9339867656857851]
0.004	27.5	2420	0.0388	0.4673	0.9346	0.9346	[0.0, 0.9345750307327253]	[nan, 0.9345750307327253]
0.0051	27.73	2440	0.0386	0.4655	0.9309	0.9309	[0.0, 0.9309002984208107]	[nan, 0.9309002984208107]
0.0045	27.95	2460	0.0395	0.4664	0.9328	0.9328	[0.0, 0.932816832956917]	[nan, 0.932816832956917]
0.0042	28.18	2480	0.0393	0.4642	0.9285	0.9285	[0.0, 0.9284856628262672]	[nan, 0.9284856628262672]
0.0035	28.41	2500	0.0396	0.4667	0.9333	0.9333	[0.0, 0.9333083366503419]	[nan, 0.9333083366503419]
0.0036	28.64	2520	0.0395	0.4664	0.9327	0.9327	[0.0, 0.9327288680900848]	[nan, 0.9327288680900848]
0.0035	28.86	2540	0.0377	0.4675	0.9349	0.9349	[0.0, 0.9349378858084081]	[nan, 0.9349378858084081]
0.0029	29.09	2560	0.0402	0.4658	0.9315	0.9315	[0.0, 0.9315479397528627]	[nan, 0.9315479397528627]
0.0042	29.32	2580	0.0398	0.4691	0.9383	0.9383	[0.0, 0.9382893472347145]	[nan, 0.9382893472347145]
0.0029	29.55	2600	0.0405	0.4668	0.9336	0.9336	[0.0, 0.9336129150017483]	[nan, 0.9336129150017483]
0.0023	29.77	2620	0.0402	0.4666	0.9332	0.9332	[0.0, 0.9332071770534849]	[nan, 0.9332071770534849]
0.0036	30.0	2640	0.0417	0.4648	0.9296	0.9296	[0.0, 0.9296435003859459]	[nan, 0.9296435003859459]
0.0045	30.23	2660	0.0395	0.4674	0.9348	0.9348	[0.0, 0.9347960424606412]	[nan, 0.9347960424606412]
0.0025	30.45	2680	0.0400	0.4695	0.9390	0.9390	[0.0, 0.9390392477244589]	[nan, 0.9390392477244589]
0.0032	30.68	2700	0.0404	0.4673	0.9347	0.9347	[0.0, 0.9346926837421135]	[nan, 0.9346926837421135]
0.0047	30.91	2720	0.0416	0.4651	0.9303	0.9303	[0.0, 0.9302790465488084]	[nan, 0.9302790465488084]
0.0024	31.14	2740	0.0403	0.4677	0.9355	0.9355	[0.0, 0.9354997613952987]	[nan, 0.9354997613952987]
0.0037	31.36	2760	0.0406	0.4677	0.9354	0.9354	[0.0, 0.9354469824751994]	[nan, 0.9354469824751994]
0.0031	31.59	2780	0.0414	0.4671	0.9343	0.9343	[0.0, 0.9342858462330146]	[nan, 0.9342858462330146]
0.0036	31.82	2800	0.0404	0.4670	0.9339	0.9339	[0.0, 0.9339152942314839]	[nan, 0.9339152942314839]
0.003	32.05	2820	0.0411	0.4678	0.9355	0.9355	[0.0, 0.9355151552469944]	[nan, 0.9355151552469944]
0.0038	32.27	2840	0.0423	0.4672	0.9344	0.9344	[0.0, 0.9344221917766045]	[nan, 0.9344221917766045]
0.0023	32.5	2860	0.0433	0.4657	0.9313	0.9313	[0.0, 0.9313401227549717]	[nan, 0.9313401227549717]
0.003	32.73	2880	0.0421	0.4682	0.9363	0.9363	[0.0, 0.9363365271910399]	[nan, 0.9363365271910399]
0.0031	32.95	2900	0.0428	0.4679	0.9357	0.9357	[0.0, 0.9357086779540251]	[nan, 0.9357086779540251]
0.0026	33.18	2920	0.0448	0.4656	0.9311	0.9311	[0.0, 0.9311081154187018]	[nan, 0.9311081154187018]
0.0031	33.41	2940	0.0456	0.4639	0.9279	0.9279	[0.0, 0.9278929995359854]	[nan, 0.9278929995359854]
0.0022	33.64	2960	0.0424	0.4674	0.9349	0.9349	[0.0, 0.9348851068883088]	[nan, 0.9348851068883088]
0.0025	33.86	2980	0.0434	0.4654	0.9308	0.9308	[0.0, 0.9307782471680811]	[nan, 0.9307782471680811]
0.0025	34.09	3000	0.0418	0.4675	0.9351	0.9351	[0.0, 0.9350610366219732]	[nan, 0.9350610366219732]
0.003	34.32	3020	0.0424	0.4674	0.9349	0.9349	[0.0, 0.9348653147932716]	[nan, 0.9348653147932716]
0.0021	34.55	3040	0.0412	0.4687	0.9374	0.9374	[0.0, 0.9374437849522901]	[nan, 0.9374437849522901]
0.0043	34.77	3060	0.0412	0.4676	0.9352	0.9352	[0.0, 0.9352446632814854]	[nan, 0.9352446632814854]
0.005	35.0	3080	0.0428	0.4675	0.9350	0.9350	[0.0, 0.9349807686809888]	[nan, 0.9349807686809888]
0.003	35.23	3100	0.0430	0.4672	0.9344	0.9344	[0.0, 0.934393603194884]	[nan, 0.934393603194884]
0.0027	35.45	3120	0.0452	0.4652	0.9303	0.9303	[0.0, 0.9303428210772617]	[nan, 0.9303428210772617]
0.0022	35.68	3140	0.0441	0.4653	0.9306	0.9306	[0.0, 0.9305847244610502]	[nan, 0.9305847244610502]
0.0029	35.91	3160	0.0425	0.4671	0.9342	0.9342	[0.0, 0.9341692927844619]	[nan, 0.9341692927844619]
0.0022	36.14	3180	0.0438	0.4679	0.9358	0.9358	[0.0, 0.9358153353550592]	[nan, 0.9358153353550592]
0.0028	36.36	3200	0.0443	0.4680	0.9359	0.9359	[0.0, 0.935929689681941]	[nan, 0.935929689681941]
0.0025	36.59	3220	0.0433	0.4682	0.9365	0.9365	[0.0, 0.9364948639513379]	[nan, 0.9364948639513379]
0.003	36.82	3240	0.0439	0.4680	0.9359	0.9359	[0.0, 0.9359340879252827]	[nan, 0.9359340879252827]
0.0027	37.05	3260	0.0462	0.4665	0.9331	0.9331	[0.0, 0.9330587363407056]	[nan, 0.9330587363407056]
0.004	37.27	3280	0.0447	0.4675	0.9350	0.9350	[0.0, 0.9349917642893428]	[nan, 0.9349917642893428]
0.0032	37.5	3300	0.0442	0.4683	0.9367	0.9367	[0.0, 0.9366916853408749]	[nan, 0.9366916853408749]
0.0019	37.73	3320	0.0454	0.4674	0.9347	0.9347	[0.0, 0.9347102767154798]	[nan, 0.9347102767154798]
0.0028	37.95	3340	0.0451	0.4674	0.9349	0.9349	[0.0, 0.9348543191849176]	[nan, 0.9348543191849176]
0.0023	38.18	3360	0.0457	0.4669	0.9337	0.9337	[0.0, 0.9337228710852885]	[nan, 0.9337228710852885]
0.0028	38.41	3380	0.0454	0.4675	0.9351	0.9351	[0.0, 0.9350764304736688]	[nan, 0.9350764304736688]
0.0024	38.64	3400	0.0467	0.4677	0.9354	0.9354	[0.0, 0.9353568184866964]	[nan, 0.9353568184866964]
0.0023	38.86	3420	0.0463	0.4669	0.9337	0.9337	[0.0, 0.9337096763552637]	[nan, 0.9337096763552637]
0.0029	39.09	3440	0.0456	0.4664	0.9328	0.9328	[0.0, 0.9328289281261064]	[nan, 0.9328289281261064]
0.0026	39.32	3460	0.0453	0.4686	0.9372	0.9372	[0.0, 0.9371578991350854]	[nan, 0.9371578991350854]
0.0037	39.55	3480	0.0458	0.4678	0.9356	0.9356	[0.0, 0.9356097174788389]	[nan, 0.9356097174788389]
0.0025	39.77	3500	0.0468	0.4671	0.9342	0.9342	[0.0, 0.9342275695087382]	[nan, 0.9342275695087382]
0.0048	40.0	3520	0.0459	0.4668	0.9335	0.9335	[0.0, 0.933527149256587]	[nan, 0.933527149256587]
0.0027	40.23	3540	0.0468	0.4658	0.9315	0.9315	[0.0, 0.9315490393136981]	[nan, 0.9315490393136981]
0.0019	40.45	3560	0.0465	0.4662	0.9324	0.9324	[0.0, 0.9323792077444268]	[nan, 0.9323792077444268]
0.0033	40.68	3580	0.0459	0.4674	0.9348	0.9348	[0.0, 0.9348015402648182]	[nan, 0.9348015402648182]
0.004	40.91	3600	0.0467	0.4667	0.9333	0.9333	[0.0, 0.9333358256712269]	[nan, 0.9333358256712269]
0.0022	41.14	3620	0.0469	0.4665	0.9331	0.9331	[0.0, 0.9330521389756931]	[nan, 0.9330521389756931]
0.0036	41.36	3640	0.0458	0.4676	0.9352	0.9352	[0.0, 0.9352479619639916]	[nan, 0.9352479619639916]
0.0024	41.59	3660	0.0468	0.4671	0.9342	0.9342	[0.0, 0.9341769897103097]	[nan, 0.9341769897103097]
0.0021	41.82	3680	0.0466	0.4658	0.9317	0.9317	[0.0, 0.9316776879314402]	[nan, 0.9316776879314402]
0.0032	42.05	3700	0.0472	0.4666	0.9332	0.9332	[0.0, 0.9331807875934351]	[nan, 0.9331807875934351]
0.0023	42.27	3720	0.0470	0.4673	0.9347	0.9347	[0.0, 0.9346827876945948]	[nan, 0.9346827876945948]
0.003	42.5	3740	0.0474	0.4661	0.9321	0.9321	[0.0, 0.9321482999689924]	[nan, 0.9321482999689924]
0.0025	42.73	3760	0.0483	0.4656	0.9313	0.9313	[0.0, 0.9312851447132016]	[nan, 0.9312851447132016]
0.0019	42.95	3780	0.0471	0.4669	0.9338	0.9338	[0.0, 0.9338130350737915]	[nan, 0.9338130350737915]
0.0032	43.18	3800	0.0463	0.4682	0.9365	0.9365	[0.0, 0.9364508815179218]	[nan, 0.9364508815179218]
0.0026	43.41	3820	0.0484	0.4657	0.9315	0.9315	[0.0, 0.9314698709335492]	[nan, 0.9314698709335492]
0.0019	43.64	3840	0.0477	0.4673	0.9345	0.9345	[0.0, 0.9345486412726757]	[nan, 0.9345486412726757]
0.003	43.86	3860	0.0472	0.4688	0.9375	0.9375	[0.0, 0.9375218537716036]	[nan, 0.9375218537716036]
0.0025	44.09	3880	0.0473	0.4670	0.9340	0.9340	[0.0, 0.9339999604158099]	[nan, 0.9339999604158099]
0.0019	44.32	3900	0.0481	0.4670	0.9340	0.9340	[0.0, 0.9340263498758595]	[nan, 0.9340263498758595]
0.0024	44.55	3920	0.0478	0.4671	0.9343	0.9343	[0.0, 0.9342561580904587]	[nan, 0.9342561580904587]
0.0021	44.77	3940	0.0479	0.4677	0.9355	0.9355	[0.0, 0.9354579780835535]	[nan, 0.9354579780835535]
0.0019	45.0	3960	0.0479	0.4682	0.9363	0.9363	[0.0, 0.9363112372918256]	[nan, 0.9363112372918256]
0.0024	45.23	3980	0.0481	0.4681	0.9362	0.9362	[0.0, 0.9362133763774748]	[nan, 0.9362133763774748]
0.0023	45.45	4000	0.0497	0.4670	0.9340	0.9340	[0.0, 0.933970272273254]	[nan, 0.933970272273254]
0.0027	45.68	4020	0.0487	0.4671	0.9343	0.9343	[0.0, 0.9342781493071667]	[nan, 0.9342781493071667]
0.0023	45.91	4040	0.0477	0.4672	0.9344	0.9344	[0.0, 0.9344309882632876]	[nan, 0.9344309882632876]
0.003	46.14	4060	0.0485	0.4678	0.9356	0.9356	[0.0, 0.9355877262621309]	[nan, 0.9355877262621309]
0.0017	46.36	4080	0.0488	0.4677	0.9354	0.9354	[0.0, 0.9353678140950504]	[nan, 0.9353678140950504]
0.0022	46.59	4100	0.0481	0.4668	0.9337	0.9337	[0.0, 0.9336634948001769]	[nan, 0.9336634948001769]
0.0032	46.82	4120	0.0487	0.4676	0.9352	0.9352	[0.0, 0.935249061524827]	[nan, 0.935249061524827]
0.0021	47.05	4140	0.0483	0.4675	0.9351	0.9351	[0.0, 0.9350885256428583]	[nan, 0.9350885256428583]
0.002	47.27	4160	0.0486	0.4673	0.9347	0.9347	[0.0, 0.9346530995520389]	[nan, 0.9346530995520389]
0.0028	47.5	4180	0.0487	0.4675	0.9349	0.9349	[0.0, 0.9349224919567125]	[nan, 0.9349224919567125]
0.0026	47.73	4200	0.0482	0.4667	0.9335	0.9335	[0.0, 0.9334589764847919]	[nan, 0.9334589764847919]
0.0022	47.95	4220	0.0490	0.4670	0.9341	0.9341	[0.0, 0.9340769296742881]	[nan, 0.9340769296742881]
0.0027	48.18	4240	0.0489	0.4679	0.9358	0.9358	[0.0, 0.9358153353550592]	[nan, 0.9358153353550592]
0.0021	48.41	4260	0.0491	0.4676	0.9353	0.9353	[0.0, 0.9352864465932307]	[nan, 0.9352864465932307]
0.0024	48.64	4280	0.0491	0.4672	0.9344	0.9344	[0.0, 0.9343804084648591]	[nan, 0.9343804084648591]
0.0025	48.86	4300	0.0493	0.4675	0.9349	0.9349	[0.0, 0.9349466822950914]	[nan, 0.9349466822950914]
0.0022	49.09	4320	0.0484	0.4677	0.9354	0.9354	[0.0, 0.9353623162908734]	[nan, 0.9353623162908734]
0.0027	49.32	4340	0.0480	0.4677	0.9354	0.9354	[0.0, 0.9354117965284665]	[nan, 0.9354117965284665]
0.0018	49.55	4360	0.0498	0.4675	0.9350	0.9350	[0.0, 0.9349983616543552]	[nan, 0.9349983616543552]
0.0021	49.77	4380	0.0493	0.4672	0.9345	0.9345	[0.0, 0.9344738711358683]	[nan, 0.9344738711358683]
0.0017	50.0	4400	0.0496	0.4672	0.9344	0.9344	[0.0, 0.9343870058298716]	[nan, 0.9343870058298716]

Framework versions

Transformers 4.21.1
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1

plant/segformer-b5-finetuned-segments-instryde-foot-test

segformer-b5-finetuned-segments-instryde-foot-test

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

plant/
segformer-b5-finetuned-segments-instryde-foot-test