SegFormer-B5 Finetuned IDD-L2_v2 Open-Source Image Segmentation Model - Empowering Road Scene Understanding Tasks

Segformer B5 Finetuned IDD L2 V2

Developed by izzako

This model is an image segmentation model based on NVIDIA's MIT-B5 architecture, fine-tuned on the IDD 20K semantic segmentation dataset, suitable for road scene understanding tasks.

Image Segmentation

Safetensors

Open Source License:Other #Traffic scene segmentation #High-precision semantic segmentation #IDD dataset optimization

Downloads 29

Release Time : 3/25/2025

Model Overview

SegFormer-B5 is an efficient semantic segmentation model, fine-tuned on the IDD 20K dataset, capable of accurately identifying various objects and regions in road scenes, including roads, pedestrians, vehicles, buildings, etc.

Model Features

High-precision road scene segmentation

Fine-tuned on the IDD 20K dataset, it can accurately identify various scene elements such as roads, sidewalks, and vehicles.

Multi-category recognition capability

Supports recognition of over 20 different road scene categories, including static elements (e.g., roads, buildings) and dynamic elements (e.g., pedestrians, vehicles).

Optimized training parameters

Trained using the Adam optimizer and linear learning rate scheduler with a learning rate of 0.0006 for 50 epochs.

Model Capabilities

Image segmentation

Road scene understanding

Multi-category object recognition

Use Cases

Autonomous driving

Road scene parsing

Used for real-time understanding and segmentation of road environments in autonomous driving systems.

Achieved a mean Intersection over Union (mIoU) of 0.7180 on the IDD 20K evaluation set.

Intelligent transportation systems

Traffic element monitoring

Identifies and counts traffic participants such as vehicles and pedestrians on the road.

Cyclist recognition accuracy reached 0.8434, and pedestrian recognition accuracy reached 0.8057.

🚀 segformer-b5-finetuned-IDD-L2_v2

This model is a fine - tuned version of [nvidia/mit - b5](https://huggingface.co/nvidia/mit - b5) on the IDD 20K Semantic Segmentation Dataset, designed for high - performance image segmentation.

[](https://wandb.ai/musa - wijanarko/huggingface/runs/voqx3zox)

🚀 Quick Start

This model is a fine - tuned version of [nvidia/mit - b5](https://huggingface.co/nvidia/mit - b5) on the IDD 20K Semantic Segmentation Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.5563
Mean Iou: 0.7180
Mean Accuracy: 0.8224
Overall Accuracy: 0.9083
Accuracy Road: 0.9716
Accuracy Parking: 0.7949
Accuracy Sidewalk: 0.8240
Accuracy Rail track: 0.6408
Accuracy Person: 0.8057
Accuracy Rider: 0.8434
Accuracy Motorcycle: 0.8762
Accuracy Autorickshaw: 0.9451
Accuracy Truck: 0.9122
Accuracy Curb: 0.8112
Accuracy Fence: 0.5699
Accuracy Billboard: 0.7605
Accuracy Pole: 0.6010
Accuracy Building: 0.8678
Accuracy Vegetation: 0.9495
Accuracy Sky: 0.9841
Iou Road: 0.9391
Iou Parking: 0.6620
Iou Sidewalk: 0.6707
Iou Rail track: 0.5025
Iou Person: 0.6726
Iou Rider: 0.7228
Iou Motorcycle: 0.7637
Iou Autorickshaw: 0.8882
Iou Truck: 0.8506
Iou Curb: 0.6721
Iou Fence: 0.4571
Iou Billboard: 0.6238
Iou Pole: 0.4831
Iou Building: 0.7293
Iou Vegetation: 0.8792
Iou Sky: 0.9707

📚 Documentation

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0006
train_batch_size: 64
eval_batch_size: 64
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	Accuracy Road	Accuracy Parking	Accuracy Sidewalk	Accuracy Rail track	Accuracy Person	Accuracy Rider	Accuracy Motorcycle	Accuracy Autorickshaw	Accuracy Truck	Accuracy Curb	Accuracy Fence	Accuracy Billboard	Accuracy Pole	Accuracy Building	Accuracy Vegetation	Accuracy Sky	Iou Road	Iou Parking	Iou Sidewalk	Iou Rail track	Iou Person	Iou Rider	Iou Motorcycle	Iou Autorickshaw	Iou Truck	Iou Curb	Iou Fence	Iou Billboard	Iou Pole	Iou Building	Iou Vegetation	Iou Sky
0.3096	1.0	202	0.3310	0.6476	0.7663	0.8841	0.9538	0.7998	0.7838	0.5755	0.6912	0.7355	0.8445	0.9518	0.8603	0.7173	0.3997	0.6938	0.4456	0.8868	0.9531	0.9690	0.9256	0.6317	0.5876	0.4378	0.5705	0.5911	0.6722	0.8070	0.7758	0.6255	0.3351	0.5507	0.3663	0.6693	0.8551	0.9600
0.2786	2.0	404	0.3369	0.6560	0.7917	0.8774	0.8968	0.8571	0.8009	0.5009	0.7221	0.8105	0.8267	0.9102	0.9216	0.8204	0.6046	0.6927	0.5244	0.8650	0.9354	0.9773	0.8834	0.5556	0.5941	0.4215	0.6054	0.6340	0.6926	0.8403	0.7757	0.6229	0.3671	0.5681	0.4130	0.6967	0.8610	0.9641
0.2541	3.0	606	0.3013	0.6796	0.7930	0.8958	0.9724	0.7579	0.8132	0.5424	0.7483	0.8020	0.8235	0.9296	0.9107	0.8067	0.5309	0.7415	0.5359	0.8420	0.9502	0.9806	0.9336	0.6384	0.6194	0.4623	0.6185	0.6505	0.7102	0.8534	0.8099	0.6446	0.3983	0.5839	0.4236	0.6975	0.8641	0.9660
0.2304	4.0	808	0.3055	0.6860	0.8016	0.8947	0.9493	0.8219	0.7667	0.5809	0.7948	0.7780	0.8393	0.9303	0.9033	0.7869	0.5968	0.7070	0.5882	0.8711	0.9259	0.9851	0.9250	0.6310	0.6435	0.4815	0.6264	0.6589	0.7188	0.8609	0.8095	0.6519	0.4044	0.5840	0.4376	0.7066	0.8697	0.9671
0.214	5.0	1010	0.3138	0.6845	0.7921	0.8967	0.9526	0.8481	0.8431	0.4857	0.7539	0.7570	0.8772	0.9463	0.8889	0.7298	0.4582	0.7475	0.5890	0.8661	0.9449	0.9850	0.9289	0.6438	0.6386	0.4351	0.6379	0.6594	0.7182	0.8578	0.8239	0.6402	0.3870	0.5965	0.4537	0.6955	0.8695	0.9666
0.2029	6.0	1212	0.3123	0.6914	0.8010	0.8988	0.9697	0.7612	0.8111	0.6453	0.7988	0.8621	0.8112	0.9377	0.9028	0.7567	0.5211	0.7047	0.5390	0.8494	0.9638	0.9822	0.9339	0.6378	0.6746	0.4754	0.6271	0.6660	0.7143	0.8682	0.8305	0.6344	0.4264	0.5947	0.4335	0.7168	0.8614	0.9679
0.1837	7.0	1414	0.3201	0.6963	0.8009	0.9008	0.9614	0.8304	0.7910	0.5696	0.7538	0.8212	0.8642	0.9357	0.9092	0.7900	0.5475	0.6914	0.5137	0.9004	0.9509	0.9841	0.9337	0.6534	0.6600	0.4621	0.6489	0.6881	0.7289	0.8715	0.8325	0.6717	0.4265	0.5842	0.4306	0.7074	0.8725	0.9683
0.1904	8.0	1616	0.3075	0.6946	0.8092	0.8997	0.9693	0.7558	0.7707	0.6996	0.7802	0.8379	0.8572	0.9428	0.8965	0.7714	0.5436	0.7467	0.6316	0.8216	0.9398	0.9827	0.9350	0.6371	0.6389	0.4958	0.6434	0.6665	0.7272	0.8646	0.8259	0.6533	0.4153	0.5983	0.4638	0.7058	0.8745	0.9687
0.166	9.0	1818	0.3127	0.7018	0.8110	0.9030	0.9725	0.7634	0.8258	0.6332	0.7766	0.8286	0.8336	0.9274	0.9101	0.8199	0.5639	0.7524	0.5788	0.8569	0.9506	0.9828	0.9342	0.6427	0.6429	0.4986	0.6563	0.6966	0.7359	0.8724	0.8262	0.6580	0.4284	0.6059	0.4654	0.7232	0.8738	0.9687
0.157	10.0	2020	0.3267	0.7024	0.8168	0.9020	0.9604	0.8061	0.7791	0.6743	0.8065	0.8319	0.8585	0.9401	0.9024	0.7924	0.5971	0.7363	0.5982	0.8637	0.9412	0.9811	0.9341	0.6525	0.6635	0.5021	0.6506	0.6902	0.7353	0.8638	0.8235	0.6649	0.4250	0.6045	0.4686	0.7166	0.8754	0.9686
0.1543	11.0	2222	0.3260	0.7009	0.8131	0.9029	0.9753	0.7483	0.8429	0.6313	0.7917	0.8380	0.8368	0.9411	0.8959	0.7904	0.5871	0.7533	0.5913	0.8523	0.9509	0.9824	0.9341	0.6377	0.6377	0.4966	0.6583	0.6951	0.7325	0.8603	0.8235	0.6620	0.4303	0.6088	0.4709	0.7213	0.8763	0.9689
0.1474	12.0	2424	0.3394	0.7054	0.8179	0.9028	0.9585	0.8439	0.8303	0.6732	0.7800	0.8231	0.8755	0.9453	0.8985	0.7896	0.5814	0.7545	0.5047	0.8994	0.9452	0.9842	0.9351	0.6593	0.6707	0.5076	0.6632	0.6995	0.7427	0.8737	0.8403	0.6595	0.4371	0.6068	0.4317	0.7151	0.8752	0.9693
0.1365	13.0	2626	0.3347	0.7121	0.8220	0.9063	0.9706	0.7858	0.8210	0.6734	0.8003	0.8490	0.8637	0.9461	0.9108	0.7885	0.6000	0.7537	0.5916	0.8648	0.9510	0.9815	0.9387	0.6604	0.6668	0.5029	0.6702	0.7067	0.7480	0.8793	0.8403	0.6713	0.4449	0.6162	0.4739	0.7280	0.8774	0.9689
0.1301	14.0	2828	0.3412	0.7148	0.8159	0.9073	0.9734	0.7725	0.8092	0.6343	0.7902	0.8236	0.8692	0.9424	0.9058	0.8016	0.5709	0.7403	0.6210	0.8633	0.9524	0.9838	0.9384	0.6549	0.6718	0.5043	0.6695	0.7121	0.7544	0.8832	0.8444	0.6744	0.4519	0.6165	0.4869	0.7252	0.8787	0.9696
0.1216	15.0	3030	0.3623	0.7137	0.8187	0.9071	0.9686	0.8045	0.8266	0.6700	0.8050	0.8177	0.8601	0.9466	0.9172	0.8057	0.5325	0.7487	0.5887	0.8770	0.9476	0.9830	0.9390	0.6654	0.6707	0.5055	0.6697	0.7121	0.7563	0.8813	0.8468	0.6702	0.4311	0.6210	0.4756	0.7260	0.8795	0.9699
0.1198	16.0	3232	0.3660	0.7154	0.8230	0.9073	0.9703	0.8029	0.8357	0.6354	0.8038	0.8289	0.8623	0.9484	0.9166	0.8085	0.6043	0.7602	0.5904	0.8719	0.9444	0.9839	0.9385	0.6613	0.6717	0.4995	0.6695	0.7130	0.7542	0.8825	0.8511	0.6757	0.4554	0.6205	0.4779	0.7279	0.8782	0.9699
0.1264	17.0	3434	0.3688	0.7093	0.8161	0.9037	0.9631	0.7818	0.7960	0.7013	0.8166	0.8203	0.8650	0.9403	0.9087	0.7833	0.5580	0.7223	0.5917	0.8796	0.9515	0.9789	0.9339	0.6401	0.6720	0.4989	0.6648	0.7075	0.7501	0.8823	0.8481	0.6613	0.4408	0.6131	0.4704	0.7195	0.8767	0.9687
0.137	18.0	3636	0.3583	0.7079	0.8131	0.9048	0.9699	0.7900	0.8168	0.6048	0.8107	0.8355	0.8609	0.9340	0.9144	0.7944	0.4969	0.7738	0.6219	0.8578	0.9431	0.9840	0.9366	0.6536	0.6658	0.5010	0.6672	0.7036	0.7478	0.8792	0.8352	0.6600	0.4237	0.6080	0.4789	0.7174	0.8782	0.9695
0.1243	19.0	3838	0.3671	0.7050	0.8140	0.9045	0.9728	0.7609	0.8216	0.6357	0.7975	0.8456	0.8801	0.9328	0.8930	0.7846	0.5696	0.7238	0.5972	0.8748	0.9539	0.9808	0.9370	0.6470	0.6367	0.4900	0.6577	0.7019	0.7449	0.8665	0.8285	0.6600	0.4545	0.6079	0.4741	0.7283	0.8765	0.9693
0.1139	20.0	4040	0.3773	0.7114	0.8169	0.9063	0.9745	0.7752	0.8003	0.6377	0.7977	0.8502	0.8648	0.9407	0.9017	0.8282	0.5676	0.7387	0.6023	0.8613	0.9465	0.9837	0.9388	0.6584	0.6593	0.5051	0.6649	0.7108	0.7508	0.8775	0.8388	0.6628	0.4501	0.6150	0.4735	0.7273	0.8790	0.9698
0.107	21.0	4242	0.3894	0.7156	0.8208	0.9075	0.9698	0.8161	0.8183	0.6471	0.7960	0.8217	0.8852	0.9457	0.9072	0.8157	0.5433	0.7691	0.6085	0.8623	0.9432	0.9830	0.9399	0.6685	0.6703	0.5096	0.6752	0.7144	0.7542	0.8816	0.8473	0.6670	0.4446	0.6210	0.4815	0.7263	0.8791	0.9701
0.1031	22.0	4444	0.4017	0.7151	0.8233	0.9073	0.9691	0.8062	0.8372	0.6409	0.8117	0.8351	0.8776	0.9459	0.9121	0.8086	0.5799	0.7538	0.6018	0.8625	0.9452	0.9853	0.9387	0.6634	0.6669	0.5031	0.6714	0.7145	0.7572	0.8839	0.8469	0.6665	0.4540	0.6171	0.4809	0.7264	0.8800	0.9702
0.0999	23.0	4646	0.4116	0.7157	0.8202	0.9077	0.9743	0.7844	0.8315	0.6443	0.7960	0.8279	0.8840	0.9517	0.8990	0.8163	0.5712	0.7390	0.6063	0.8684	0.9476	0.9821	0.9395	0.6627	0.6683	0.5113	0.6759	0.7177	0.7575	0.8790	0.8417	0.6645	0.4521	0.6198	0.4838	0.7280	0.8797	0.9703
0.0963	24.0	4848	0.4264	0.7153	0.8177	0.9074	0.9754	0.7781	0.8163	0.6371	0.8127	0.8306	0.8785	0.9477	0.9128	0.7785	0.5563	0.7605	0.5915	0.8720	0.9529	0.9827	0.9389	0.6555	0.6780	0.5042	0.6689	0.7179	0.7590	0.8842	0.8467	0.6665	0.4506	0.6248	0.4772	0.7244	0.8784	0.9703
0.096	25.0	5050	0.4291	0.7157	0.8208	0.9078	0.9727	0.7987	0.8335	0.6217	0.8103	0.8348	0.8773	0.9458	0.9083	0.8045	0.5717	0.7563	0.5977	0.8668	0.9508	0.9825	0.9392	0.6624	0.6659	0.5007	0.6680	0.7168	0.7586	0.8855	0.8497	0.6674	0.4571	0.6224	0.4799	0.7274	0.8794	0.9702
0.0926	26.0	5252	0.4360	0.7169	0.8230	0.9081	0.9721	0.7966	0.8304	0.6249	0.8104	0.8352	0.8746	0.9530	0.9080	0.8115	0.5851	0.7736	0.5962	0.8616	0.9495	0.9848	0.9396	0.6643	0.6679	0.5000	0.6711	0.7208	0.7626	0.8836	0.8481	0.6672	0.4583	0.6277	0.4808	0.7288	0.8794	0.9704
0.0903	27.0	5454	0.4453	0.7166	0.8226	0.9077	0.9726	0.7853	0.8258	0.6282	0.8004	0.8374	0.8824	0.9457	0.9191	0.8191	0.5907	0.7600	0.5891	0.8771	0.9470	0.9820	0.9392	0.6602	0.6705	0.5029	0.6762	0.7191	0.7619	0.8867	0.8472	0.6657	0.4559	0.6253	0.4776	0.7277	0.8797	0.9702
0.093	28.0	5656	0.4438	0.7152	0.8195	0.9072	0.9696	0.7980	0.8227	0.6421	0.7945	0.8561	0.8705	0.9487	0.9011	0.8025	0.5575	0.7460	0.5975	0.8694	0.9504	0.9847	0.9372	0.6581	0.6650	0.5061	0.6746	0.7209	0.7549	0.8822	0.8461	0.6640	0.4536	0.6201	0.4814	0.7290	0.8786	0.9706
0.0883	29.0	5858	0.4514	0.7173	0.8231	0.9079	0.9714	0.7987	0.8199	0.6475	0.8239	0.8308	0.8722	0.9480	0.9129	0.8073	0.5782	0.7659	0.5917	0.8685	0.9516	0.9807	0.9395	0.6652	0.6729	0.5059	0.6700	0.7222	0.7621	0.8844	0.8489	0.6674	0.4604	0.6219	0.4785	0.7282	0.8790	0.9700
0.0857	30.0	6060	0.4478	0.7175	0.8225	0.9081	0.9691	0.8046	0.8208	0.6461	0.8021	0.8401	0.8844	0.9456	0.9117	0.8035	0.5546	0.7730	0.6106	0.8596	0.9510	0.9836	0.9387	0.6648	0.6688	0.5043	0.6730	0.7209	0.7629	0.8874	0.8509	0.6693	0.4518	0.6229	0.4867	0.7293	0.8788	0.9703
0.0852	31.0	6262	0.4704	0.7165	0.8207	0.9078	0.9735	0.7878	0.8214	0.6576	0.8043	0.8423	0.8666	0.9484	0.9023	0.8110	0.5632	0.7524	0.5969	0.8748	0.9448	0.9844	0.9387	0.6608	0.6739	0.5054	0.6715	0.7218	0.7640	0.8829	0.8412	0.6700	0.4513	0.6232	0.4796	0.7300	0.8799	0.9706
0.0813	32.0	6464	0.4642	0.7173	0.8239	0.9080	0.9698	0.7989	0.8283	0.6422	0.8068	0.8390	0.8813	0.9479	0.9081	0.8147	0.5751	0.7772	0.5907	0.8678	0.9499	0.9842	0.9388	0.6639	0.6757	0.5069	0.6708	0.7210	0.7603	0.8852	0.8482	0.6683	0.4588	0.6207	0.4786	0.7296	0.8797	0.9708
0.0807	33.0	6666	0.4811	0.7177	0.8218	0.9079	0.9710	0.8003	0.8216	0.6509	0.7969	0.8468	0.8685	0.9483	0.9088	0.8122	0.5717	0.7543	0.5995	0.8662	0.9477	0.9843	0.9387	0.6627	0.6763	0.5087	0.6744	0.7237	0.7632	0.8847	0.8476	0.6656	0.4574	0.6202	0.4806	0.7295	0.8799	0.9707
0.0804	34.0	6868	0.4824	0.7176	0.8220	0.9079	0.9699	0.7993	0.8269	0.6392	0.8077	0.8321	0.8793	0.9479	0.9114	0.8028	0.5786	0.7535	0.5934	0.8784	0.9469	0.9849	0.9384	0.6621	0.6708	0.5042	0.6736	0.7208	0.7631	0.8879	0.8515	0.6696	0.4597	0.6227	0.4789	0.7280	0.8794	0.9706
0.0769	35.0	7070	0.4960	0.7178	0.8214	0.9081	0.9694	0.8053	0.8223	0.6416	0.7990	0.8403	0.8779	0.9476	0.9132	0.8100	0.5511	0.7574	0.6043	0.8715	0.9481	0.9829	0.9386	0.6632	0.6749	0.5031	0.6727	0.7218	0.7654	0.8878	0.8495	0.6725	0.4490	0.6229	0.4840	0.7285	0.8798	0.9706
0.0776	36.0	7272	0.4918	0.7168	0.8219	0.9077	0.9697	0.8043	0.8247	0.6536	0.8009	0.8346	0.8787	0.9517	0.9077	0.8046	0.5543	0.7730	0.5920	0.8680	0.9473	0.9847	0.9387	0.6631	0.6732	0.5020	0.6755	0.7227	0.7651	0.8844	0.8483	0.6669	0.4485	0.6235	0.4788	0.7277	0.8795	0.9704
0.0782	37.0	7474	0.4938	0.7171	0.8237	0.9081	0.9731	0.7905	0.8343	0.6444	0.8066	0.8508	0.8759	0.9471	0.9091	0.8169	0.5733	0.7636	0.5973	0.8642	0.9479	0.9843	0.9392	0.6621	0.6678	0.5046	0.6731	0.7221	0.7623	0.8866	0.8501	0.6702	0.4521	0.6232	0.4812	0.7300	0.8791	0.9706
0.0758	38.0	7676	0.5168	0.7151	0.8202	0.9069	0.9760	0.7477	0.8282	0.6616	0.8080	0.8463	0.8742	0.9456	0.9139	0.8158	0.5488	0.7629	0.5967	0.8654	0.9481	0.9845	0.9359	0.6395	0.6683	0.5066	0.6746	0.7230	0.7636	0.8883	0.8498	0.6677	0.4439	0.6222	0.4794	0.7285	0.8797	0.9706
0.0745	39.0	7878	0.5100	0.7173	0.8203	0.9081	0.9725	0.7948	0.8194	0.6488	0.8033	0.8417	0.8719	0.9457	0.9111	0.8140	0.5465	0.7558	0.5942	0.8723	0.9471	0.9850	0.9388	0.6622	0.6752	0.5043	0.6732	0.7239	0.7649	0.8869	0.8501	0.6683	0.4472	0.6225	0.4804	0.7295	0.8793	0.9708
0.0719	40.0	8080	0.5147	0.7181	0.8233	0.9083	0.9706	0.8003	0.8253	0.6494	0.8033	0.8491	0.8741	0.9453	0.9125	0.8069	0.5725	0.7652	0.5952	0.8685	0.9493	0.9847	0.9391	0.6636	0.6737	0.5047	0.6761	0.7237	0.7627	0.8859	0.8501	0.6725	0.4535	0.6223	0.4818	0.7291	0.8796	0.9706
0.0731	41.0	8282	0.5221	0.7173	0.8217	0.9083	0.9726	0.7939	0.8299	0.6356	0.8109	0.8419	0.8685	0.9448	0.9132	0.8095	0.5581	0.7649	0.6038	0.8674	0.9478	0.9846	0.9393	0.6625	0.6710	0.5008	0.6714	0.7237	0.7655	0.8859	0.8475	0.6690	0.4523	0.6234	0.4846	0.7296	0.8798	0.9708
0.0723	42.0	8484	0.5245	0.7176	0.8230	0.9084	0.9711	0.7990	0.8275	0.6417	0.8100	0.8396	0.8831	0.9465	0.9126	0.8083	0.5616	0.7649	0.6050	0.8640	0.9497	0.9839	0.9396	0.6645	0.6709	0.5026	0.6707	0.7224	0.7624	0.8864	0.8493	0.6709	0.4531	0.6237	0.4845	0.7304	0.8794	0.9706
0.0706	43.0	8686	0.5327	0.7176	0.8226	0.9081	0.9710	0.7981	0.8270	0.6415	0.8121	0.8409	0.8774	0.9466	0.9109	0.8068	0.5685	0.7562	0.6078	0.8638	0.9498	0.9833	0.9389	0.6618	0.6714	0.5029	0.6722	0.7222	0.7637	0.8858	0.8482

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