SegFormer-B5 Image Segmentation Model Open-Source! Free for Branch and Background Semantic Segmentation Tasks

Segformer B5 Finetuned 100by100PNG 50epochs Attempt2 100epochs Backgroundclass 2

Developed by JCAI2000

This model is an image segmentation model fine-tuned on the JCAI2000/100By100BranchPNG dataset based on nvidia/mit-b5, primarily used for semantic segmentation tasks of branches and backgrounds.

Image Segmentation

Transformers

Open Source License:Other #High-precision image segmentation #Small-size image processing #Plant branch recognition

Downloads 16

Release Time : 9/6/2023

Model Overview

This is a semantic segmentation model based on the SegFormer architecture, fine-tuned specifically for processing 100x100 pixel PNG images, capable of accurately distinguishing between branch and background regions in images.

Model Features

High-precision segmentation

Achieved a mean Intersection over Union (mIoU) of 0.8933 and an average accuracy of 0.9531 on the evaluation set

Strong background recognition

Background recognition accuracy reached 0.9732 with an IoU of 0.9597

Optimized for small-size images

Specifically optimized for small 100x100 pixel PNG images

Model Capabilities

Image semantic segmentation

Background recognition

Branch detection

Use Cases

Agricultural image analysis

Plant branch detection

Used to identify and analyze branch structures in plant images

Branch recognition accuracy 93.3%, IoU 82.7%

Remote sensing image processing

Land cover classification

Used to distinguish land cover types such as vegetation and non-vegetation areas

🚀 segformer-b3-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass

This model is a fine - tuned version of nvidia/mit-b5 for image segmentation tasks, achieving high accuracy on the JCAI2000/100By100BranchPNG dataset.

🚀 Quick Start

This model is a fine-tuned version of nvidia/mit-b5 on the JCAI2000/100By100BranchPNG dataset. It achieves the following results on the evaluation set:

Loss: 0.1497
Mean Iou: 0.8933
Mean Accuracy: 0.9531
Overall Accuracy: 0.9662
Accuracy Background: 0.9732
Accuracy Branch: 0.9330
Iou Background: 0.9597
Iou Branch: 0.8270

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Branch	Iou Background	Iou Branch
0.2055	1.05	20	0.2925	0.8151	0.9469	0.9320	0.9242	0.9695	0.9183	0.7118
0.1549	2.11	40	0.1328	0.8802	0.9311	0.9628	0.9796	0.8825	0.9561	0.8043
0.0735	3.16	60	0.1178	0.8804	0.9512	0.9613	0.9666	0.9357	0.9538	0.8070
0.0636	4.21	80	0.0844	0.8966	0.9368	0.9686	0.9854	0.8881	0.9629	0.8303
0.0546	5.26	100	0.1099	0.8969	0.9526	0.9676	0.9756	0.9297	0.9614	0.8325
0.0567	6.32	120	0.1012	0.8996	0.9500	0.9688	0.9788	0.9213	0.9629	0.8364
0.0515	7.37	140	0.1137	0.8935	0.9462	0.9668	0.9777	0.9147	0.9605	0.8265
0.052	8.42	160	0.0987	0.8914	0.9317	0.9670	0.9858	0.8776	0.9611	0.8217
0.0358	9.47	180	0.1167	0.8978	0.9581	0.9676	0.9726	0.9435	0.9613	0.8344
0.0254	10.53	200	0.0767	0.9111	0.9519	0.9729	0.9840	0.9197	0.9678	0.8545
0.0483	11.58	220	0.0953	0.9037	0.9524	0.9701	0.9795	0.9253	0.9645	0.8429
0.0285	12.63	240	0.0904	0.9026	0.9490	0.9700	0.9811	0.9169	0.9643	0.8409
0.0389	13.68	260	0.0902	0.9025	0.9472	0.9701	0.9821	0.9123	0.9644	0.8406
0.0473	14.74	280	0.0852	0.9084	0.9522	0.9719	0.9823	0.9220	0.9665	0.8502
0.0266	15.79	300	0.0983	0.8985	0.9409	0.9690	0.9839	0.8979	0.9633	0.8337
0.0233	16.84	320	0.0965	0.9052	0.9601	0.9702	0.9756	0.9447	0.9644	0.8460
0.0257	17.89	340	0.0941	0.9039	0.9550	0.9701	0.9781	0.9319	0.9643	0.8434
0.0352	18.95	360	0.0855	0.9043	0.9483	0.9706	0.9824	0.9142	0.9651	0.8435
0.1941	20.0	380	0.0946	0.9045	0.9509	0.9706	0.9809	0.9210	0.9650	0.8441
0.0325	21.05	400	0.0972	0.8973	0.9449	0.9683	0.9807	0.9092	0.9624	0.8323
0.0159	22.11	420	0.0828	0.9081	0.9528	0.9717	0.9817	0.9239	0.9664	0.8498
0.0175	23.16	440	0.1061	0.8995	0.9491	0.9688	0.9793	0.9188	0.9629	0.8360
0.0281	24.21	460	0.1090	0.8969	0.9516	0.9677	0.9761	0.9271	0.9615	0.8323
0.0177	25.26	480	0.1122	0.8983	0.9547	0.9680	0.9750	0.9343	0.9618	0.8347
0.0228	26.32	500	0.1088	0.8957	0.9546	0.9670	0.9736	0.9357	0.9606	0.8307
0.0348	27.37	520	0.0933	0.9059	0.9524	0.9710	0.9808	0.9241	0.9654	0.8464
0.0177	28.42	540	0.1053	0.9025	0.9527	0.9697	0.9787	0.9268	0.9639	0.8411
0.0182	29.47	560	0.1039	0.8992	0.9473	0.9688	0.9802	0.9143	0.9630	0.8355
0.0171	30.53	580	0.1117	0.8991	0.9555	0.9682	0.9750	0.9360	0.9621	0.8361
0.0275	31.58	600	0.1142	0.8935	0.9497	0.9665	0.9754	0.9241	0.9601	0.8268
0.0186	32.63	620	0.1065	0.9024	0.9524	0.9697	0.9788	0.9261	0.9639	0.8408
0.0173	33.68	640	0.1081	0.8986	0.9529	0.9682	0.9764	0.9294	0.9621	0.8351
0.015	34.74	660	0.1243	0.8935	0.9530	0.9663	0.9733	0.9327	0.9598	0.8272
0.0183	35.79	680	0.1120	0.9005	0.9500	0.9691	0.9792	0.9209	0.9633	0.8377
0.0248	36.84	700	0.1185	0.8962	0.9517	0.9674	0.9757	0.9277	0.9611	0.8312
0.0104	37.89	720	0.1136	0.8975	0.9506	0.9680	0.9771	0.9241	0.9619	0.8332
0.0481	38.95	740	0.1127	0.9010	0.9528	0.9691	0.9778	0.9277	0.9632	0.8388
0.0153	40.0	760	0.1101	0.9019	0.9537	0.9694	0.9777	0.9297	0.9635	0.8402
0.0143	41.05	780	0.1105	0.9032	0.9558	0.9698	0.9771	0.9345	0.9639	0.8425
0.0104	42.11	800	0.1122	0.8986	0.9428	0.9689	0.9827	0.9028	0.9631	0.8340
0.0172	43.16	820	0.1097	0.9041	0.9540	0.9702	0.9788	0.9291	0.9645	0.8437
0.0371	44.21	840	0.1064	0.9011	0.9503	0.9693	0.9794	0.9212	0.9635	0.8387
0.0221	45.26	860	0.1150	0.9004	0.9515	0.9690	0.9783	0.9247	0.9631	0.8377
0.0186	46.32	880	0.1228	0.8958	0.9518	0.9672	0.9754	0.9282	0.9610	0.8306
0.0119	47.37	900	0.1205	0.8980	0.9525	0.9680	0.9762	0.9288	0.9619	0.8340
0.0113	48.42	920	0.1133	0.8998	0.9502	0.9688	0.9787	0.9216	0.9629	0.8366
0.0121	49.47	940	0.1145	0.8993	0.9490	0.9688	0.9792	0.9188	0.9629	0.8358
0.0263	50.53	960	0.1168	0.8977	0.9542	0.9678	0.9750	0.9334	0.9616	0.8338
0.0093	51.58	980	0.1213	0.8940	0.9534	0.9664	0.9733	0.9334	0.9600	0.8280
0.0193	52.63	1000	0.1241	0.8971	0.9507	0.9678	0.9769	0.9246	0.9617	0.8326
0.0139	53.68	1020	0.1263	0.8962	0.9546	0.9672	0.9739	0.9353	0.9609	0.8316
0.012	54.74	1040	0.1252	0.8952	0.9504	0.9671	0.9760	0.9247	0.9609	0.8296
0.008	55.79	1060	0.1219	0.8986	0.9516	0.9683	0.9772	0.9260	0.9623	0.8349
0.0092	56.84	1080	0.1290	0.8995	0.9552	0.9684	0.9754	0.9349	0.9623	0.8366
0.015	57.89	1100	0.1243	0.8989	0.9545	0.9682	0.9755	0.9335	0.9621	0.8358
0.0126	58.95	1120	0.1214	0.8977	0.9541	0.9678	0.9751	0.9331	0.9616	0.8337
0.0212	60.0	1140	0.1298	0.8953	0.9542	0.9669	0.9736	0.9347	0.9605	0.8301
0.0192	61.05	1160	0.1341	0.8930	0.9518	0.9661	0.9737	0.9299	0.9597	0.8262
0.0136	62.11	1180	0.1327	0.8970	0.9528	0.9676	0.9754	0.9302	0.9614	0.8325
0.0131	63.16	1200	0.1233	0.8997	0.9549	0.9685	0.9757	0.9340	0.9624	0.8369
0.0135	64.21	1220	0.1301	0.8957	0.9542	0.9670	0.9738	0.9345	0.9607	0.8307
0.0228	65.26	1240	0.1274	0.8979	0.9524	0.9680	0.9762	0.9285	0.9618	0.8339
0.0138	66.32	1260	0.1336	0.8965	0.9520	0.9675	0.9757	0.9283	0.9613	0.8318
0.0127	67.37	1280	0.1278	0.8980	0.9519	0.9681	0.9767	0.9271	0.9620	0.8341
0.0107	68.42	1300	0.1293	0.8970	0.9530	0.9676	0.9753	0.9308	0.9614	0.8327
0.0278	69.47	1320	0.1413	0.8926	0.9534	0.9659	0.9725	0.9343	0.9593	0.8258
0.0159	70.53	1340	0.1360	0.8953	0.9522	0.9670	0.9748	0.9296	0.9607	0.8298
0.0105	71.58	1360	0.1319	0.8972	0.9537	0.9676	0.9750	0.9324

📄 License

The license for this model is other.

Property	Details
Model Type	Segformer B3 fine - tuned on 100by100PNG dataset
Training Data	JCAI2000/100By100BranchPNG dataset

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