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Hasano20/
BEiT_beit-base-finetuned-ade-640-640_Clean-Set3_RGB

Name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set3_RGB
Availability: InStock

transformers

safetensors

beit

generated_from_trainer

image-segmentation

microsoft/beit-base-finetuned-ade-640-640

finetune

apache-2.0

endpoints_compatible

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BEiT_beit-base-finetuned-ade-640-640_Clean-Set3_RGB

This model is a fine-tuned version of microsoft/beit-base-finetuned-ade-640-640 on an unknown dataset. It achieves the following results on the evaluation set:

TrainLoss: 0.0216
Loss: 0.0336
Mean Iou: 0.9671
Mean Accuracy: 0.9806
Overall Accuracy: 0.9926
Accuracy Background: 0.9956
Accuracy Melt: 0.9505
Accuracy Substrate: 0.9957
Iou Background: 0.9916
Iou Melt: 0.9208
Iou Substrate: 0.9888

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 200
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Melt	Accuracy Substrate	Iou Background	Iou Melt	Iou Substrate
0.4042	0.9434	50	0.3272	0.8363	0.8672	0.9671	0.9931	0.6175	0.9911	0.9836	0.5790	0.9463
0.1649	1.8868	100	0.0973	0.9371	0.9572	0.9867	0.9959	0.8833	0.9926	0.9881	0.8437	0.9795
0.1439	2.8302	150	0.0724	0.9495	0.9800	0.9887	0.9946	0.9575	0.9879	0.9898	0.8770	0.9818
0.1275	3.7736	200	0.0656	0.9443	0.9778	0.9877	0.9969	0.9515	0.9850	0.9903	0.8627	0.9799
0.1522	4.7170	250	0.0585	0.9567	0.9737	0.9899	0.9971	0.9325	0.9915	0.9887	0.8976	0.9839
0.1292	5.6604	300	0.0594	0.9502	0.9748	0.9877	0.9934	0.9418	0.9890	0.9857	0.8850	0.9801
0.097	6.6038	350	0.0450	0.9634	0.9775	0.9912	0.9949	0.9432	0.9943	0.9883	0.9154	0.9866
0.1125	7.5472	400	0.0451	0.9605	0.9757	0.9905	0.9953	0.9384	0.9934	0.9877	0.9080	0.9857
0.102	8.4906	450	0.0518	0.9531	0.9798	0.9876	0.9921	0.9596	0.9876	0.9824	0.8960	0.9808
0.0878	9.4340	500	0.0411	0.9639	0.9820	0.9911	0.9947	0.9592	0.9922	0.9885	0.9172	0.9859
0.1198	10.3774	550	0.0679	0.9398	0.9655	0.9821	0.9873	0.9237	0.9855	0.9708	0.8768	0.9719
0.055	11.3208	600	0.0521	0.9518	0.9791	0.9867	0.9846	0.9610	0.9917	0.9780	0.8966	0.9810
0.086	12.2642	650	0.0402	0.9631	0.9791	0.9903	0.9920	0.9514	0.9940	0.9861	0.9185	0.9848
0.058	13.2075	700	0.0455	0.9590	0.9768	0.9892	0.9908	0.9463	0.9934	0.9837	0.9096	0.9836
0.0494	14.1509	750	0.0441	0.9588	0.9796	0.9895	0.9926	0.9547	0.9914	0.9842	0.9076	0.9846
0.0599	15.0943	800	0.0401	0.9622	0.9787	0.9904	0.9925	0.9496	0.9939	0.9865	0.9149	0.9851
0.0422	16.0377	850	0.0393	0.9619	0.9807	0.9906	0.9946	0.9556	0.9919	0.9880	0.9123	0.9853
0.0454	16.9811	900	0.0429	0.9579	0.9742	0.9897	0.9918	0.9360	0.9948	0.9857	0.9033	0.9846
0.0806	17.9245	950	0.0377	0.9640	0.9779	0.9915	0.9928	0.9445	0.9964	0.9892	0.9157	0.9869
0.0677	18.8679	1000	0.0380	0.9602	0.9797	0.9910	0.9941	0.9513	0.9937	0.9882	0.9047	0.9877
0.036	19.8113	1050	0.0388	0.9618	0.9799	0.9906	0.9942	0.9529	0.9925	0.9868	0.9127	0.9860
0.0424	20.7547	1100	0.0375	0.9601	0.9753	0.9905	0.9934	0.9376	0.9949	0.9868	0.9071	0.9863
0.0274	21.6981	1150	0.0322	0.9675	0.9795	0.9927	0.9955	0.9464	0.9965	0.9917	0.9218	0.9890
0.0622	22.6415	1200	0.0360	0.9648	0.9798	0.9913	0.9932	0.9512	0.9949	0.9881	0.9197	0.9868
0.0296	23.5849	1250	0.0334	0.9670	0.9823	0.9925	0.9953	0.9567	0.9950	0.9917	0.9207	0.9885
0.0222	24.5283	1300	0.0326	0.9674	0.9823	0.9925	0.9948	0.9569	0.9953	0.9912	0.9222	0.9887
0.0719	25.4717	1350	0.0328	0.9671	0.9832	0.9923	0.9945	0.9603	0.9947	0.9907	0.9223	0.9883
0.0197	26.4151	1400	0.0311	0.9681	0.9817	0.9929	0.9962	0.9537	0.9954	0.9922	0.9230	0.9893
0.0223	27.3585	1450	0.0324	0.9664	0.9811	0.9925	0.9956	0.9527	0.9950	0.9916	0.9191	0.9885
0.024	28.3019	1500	0.0340	0.9657	0.9808	0.9920	0.9950	0.9528	0.9947	0.9902	0.9190	0.9880
0.0242	29.2453	1550	0.0325	0.9672	0.9810	0.9926	0.9953	0.9522	0.9957	0.9915	0.9212	0.9888
0.0371	30.1887	1600	0.0315	0.9681	0.9826	0.9928	0.9957	0.9569	0.9952	0.9920	0.9232	0.9891
0.0235	31.1321	1650	0.0370	0.9632	0.9799	0.9911	0.9937	0.9520	0.9941	0.9880	0.9150	0.9868
0.0266	32.0755	1700	0.0335	0.9664	0.9811	0.9925	0.9951	0.9527	0.9954	0.9913	0.9193	0.9887
0.0216	33.0189	1750	0.0344	0.9656	0.9800	0.9921	0.9946	0.9497	0.9956	0.9904	0.9182	0.9883
0.0382	33.9623	1800	0.0319	0.9680	0.9819	0.9929	0.9954	0.9544	0.9959	0.9922	0.9224	0.9893
0.0161	34.9057	1850	0.0336	0.9672	0.9799	0.9927	0.9955	0.9479	0.9963	0.9920	0.9206	0.9890
0.0216	35.8491	1900	0.0336	0.9671	0.9806	0.9926	0.9956	0.9505	0.9957	0.9916	0.9208	0.9888

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.19.2
Tokenizers 0.19.1

Hasano20/BEiT_beit-base-finetuned-ade-640-640_Clean-Set3_RGB

BEiT_beit-base-finetuned-ade-640-640_Clean-Set3_RGB

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hasano20/
BEiT_beit-base-finetuned-ade-640-640_Clean-Set3_RGB