segformer-breastcancer

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1986
• Mean Iou: 0.4951
• Mean Accuracy: 0.5647
• Overall Accuracy: 0.5716
• Per Category Iou: [0.41886373003284666, 0.5713219432574086]
• Per Category Accuracy: [0.542773911636187, 0.5866474640793707]

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
0.9179	1.25	20	0.8275	0.1056	0.2990	0.2215	[0.15928433223106872, 0.05189369644942194]	[0.5449101796407185, 0.053152424747755486]
0.7951	2.5	40	0.7554	0.3808	0.6154	0.6539	[0.2962250026735109, 0.46535774064135604]	[0.4931218643793494, 0.7375983290380178]
0.6317	3.75	60	0.5784	0.2076	0.3576	0.3005	[0.24602488191071786, 0.16910477266308951]	[0.5386308464152776, 0.17651220374955784]
0.5525	5.0	80	0.4935	0.3310	0.4279	0.3908	[0.3572223576675606, 0.30487703968490387]	[0.5453956950962939, 0.31031549514039786]
0.4365	6.25	100	0.4277	0.4259	0.5007	0.5093	[0.3753112405986087, 0.4765198093920762]	[0.473248098397799, 0.528071150639244]
0.3658	7.5	120	0.3757	0.3739	0.4207	0.4501	[0.2934911929427469, 0.45430117531467024]	[0.32736688784592977, 0.5140397864133273]
0.357	8.75	140	0.3155	0.4305	0.5273	0.5652	[0.31276016750127367, 0.5482260296446353]	[0.40734746722770676, 0.6473124799973049]
0.2889	10.0	160	0.3121	0.4761	0.5439	0.5495	[0.39972203089638886, 0.5525428502787649]	[0.5259588930247613, 0.56174305590648]
0.2536	11.25	180	0.2611	0.4607	0.5411	0.5586	[0.37248963582652733, 0.5489196143472734]	[0.4856772940605276, 0.5965098455370829]
0.3375	12.5	200	0.2522	0.3905	0.4676	0.4535	[0.3615823724169426, 0.4193968866718472]	[0.512558666450882, 0.4227348526959422]
0.1835	13.75	220	0.2393	0.4343	0.4809	0.5004	[0.3816968232451229, 0.4869246466631396]	[0.41924259588930246, 0.5425994239223811]
0.1878	15.0	240	0.2364	0.3883	0.4769	0.4591	[0.3594858252766199, 0.4170536161683648]	[0.5331607056157954, 0.42058719490626106]
0.1804	16.25	260	0.2388	0.3503	0.4221	0.3934	[0.3722368961671656, 0.3283766624340039]	[0.5131736526946108, 0.3310593427324945]
0.2296	17.5	280	0.2108	0.3845	0.4523	0.4383	[0.36382381172455475, 0.4051134890024848]	[0.4968765172357987, 0.40781915879192143]
0.1752	18.75	300	0.2065	0.4408	0.5307	0.5278	[0.37362255868123995, 0.5080655748465653]	[0.539941738145331, 0.5215102666464534]
0.1404	20.0	320	0.2025	0.4192	0.5049	0.4948	[0.37603680369849973, 0.4624047452321127]	[0.5370771969574365, 0.4727289571647548]
0.1044	21.25	340	0.1993	0.4134	0.5006	0.4938	[0.36164057945015027, 0.46514651056315]	[0.5219938501375627, 0.4791635083463877]
0.1047	22.5	360	0.1995	0.4409	0.5612	0.5654	[0.35316826827766823, 0.5286988461568266]	[0.5477909046771322, 0.5746205804571564]
0.0969	23.75	380	0.1934	0.4208	0.5256	0.5171	[0.3610564616784075, 0.480532337904731]	[0.5524356692021363, 0.49872824970101237]
0.1198	25.0	400	0.2100	0.4047	0.4892	0.4726	[0.377810637529348, 0.43159533203482664]	[0.5416895937854022, 0.4366988394225748]
0.116	26.25	420	0.2038	0.4208	0.5123	0.5040	[0.3659432240473206, 0.47558361909786334]	[0.5386632141123159, 0.48590968046220967]
0.0803	27.5	440	0.2035	0.4643	0.5486	0.5520	[0.3885018236229309, 0.5400125204269953]	[0.537854021686357, 0.5594101099937676]
0.1031	28.75	460	0.2068	0.4193	0.5268	0.5199	[0.3565531095848628, 0.48207738324971056]	[0.5486324648001295, 0.5049522461973824]
0.0652	30.0	480	0.1906	0.4799	0.5572	0.5719	[0.39256244632789455, 0.5671483599490623]	[0.5104709499919081, 0.6039045260835144]
0.0865	31.25	500	0.1946	0.4660	0.5319	0.5360	[0.4022848534304187, 0.5297039831736081]	[0.5185952419485353, 0.5451176579581248]
0.0781	32.5	520	0.2018	0.4170	0.4977	0.4881	[0.37508619500758517, 0.4588260589120619]	[0.5281922641204079, 0.46729664628497314]
0.0922	33.75	540	0.1932	0.4649	0.5558	0.5608	[0.39512968947922955, 0.5346638407173079]	[0.5401683120245995, 0.571521215490087]
0.0802	35.0	560	0.2029	0.4519	0.5364	0.5344	[0.3877223005943433, 0.5161263869184783]	[0.5426606246965529, 0.5300756312429464]
0.0737	36.25	580	0.1983	0.4605	0.5598	0.5666	[0.3930664524057094, 0.5280028151990147]	[0.5383719048389707, 0.5812993750736941]
0.0766	37.5	600	0.2097	0.4902	0.5645	0.5701	[0.41298901286924217, 0.5674679408239331]	[0.5468846091600582, 0.5821500160021561]
0.0663	38.75	620	0.1926	0.5041	0.5653	0.5781	[0.42229021548076295, 0.5859655697770101]	[0.5249069428710147, 0.6057405629390065]
0.0572	40.0	640	0.1944	0.4884	0.5550	0.5643	[0.41379925802215733, 0.5630840363400389]	[0.525295355235475, 0.5846429834756683]
0.1065	41.25	660	0.1949	0.4713	0.5603	0.5687	[0.4052270716602772, 0.537297205601135]	[0.5337271403139666, 0.5868664409520441]
0.0881	42.5	680	0.1945	0.4557	0.5355	0.5362	[0.38861418270649184, 0.5228113541121006]	[0.5329341317365269, 0.5379672208465983]
0.0616	43.75	700	0.2055	0.4851	0.5479	0.5493	[0.4288067420034476, 0.5413945423770796]	[0.543486000971031, 0.5522512506948305]
0.135	45.0	720	0.2017	0.4950	0.5702	0.5770	[0.4186215922560253, 0.5714192766576933]	[0.5487133840427254, 0.5917428874627318]
0.0683	46.25	740	0.1986	0.4880	0.5579	0.5633	[0.41617258731503165, 0.5599071727881785]	[0.5407347467227707, 0.5750585342025031]
0.0962	47.5	760	0.2010	0.4907	0.5660	0.5730	[0.41037067786677084, 0.571094427269902]	[0.543955332578087, 0.5881213468762106]
0.0534	48.75	780	0.2061	0.4941	0.5671	0.5740	[0.4158937943809818, 0.5723742349360128]	[0.5450234665803528, 0.5891404315528829]
0.069	50.0	800	0.1986	0.4951	0.5647	0.5716	[0.41886373003284666, 0.5713219432574086]	[0.542773911636187, 0.5866474640793707]

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2