You can deploy this model and start earning money today!

BigR-Oclock/
segformer-b0-crop-detection

Name: segformer-b0-crop-detection
Availability: InStock

generated_from_trainer

nvidia/segformer-b0-finetuned-ade-512-512

finetune

other

endpoints_compatible

Views

No views yet

segformer-b0-crop-detection

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

Loss: 0.2364
Mean Iou: 0.4754
Mean Accuracy: 0.9509
Overall Accuracy: 0.9509
Accuracy Background: nan
Accuracy Crop: 0.9509
Iou Background: 0.0
Iou Crop: 0.9509

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: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Crop	Iou Crop
0.5159	0.1092	50	0.3885	0.4099	0.8197	0.8197	nan	0.8197	0.8197
0.3496	0.2183	100	0.2894	0.4077	0.8155	0.8155	nan	0.8155	0.8155
0.3076	0.3275	150	0.2679	0.4386	0.8773	0.8773	nan	0.8773	0.8773
0.2953	0.4367	200	0.2906	0.4444	0.8888	0.8888	nan	0.8888	0.8888
0.2322	0.5459	250	0.2511	0.3949	0.7898	0.7898	nan	0.7898	0.7898
0.2256	0.6550	300	0.2468	0.4529	0.9058	0.9058	nan	0.9058	0.9058
0.2706	0.7642	350	0.1816	0.4332	0.8663	0.8663	nan	0.8663	0.8663
0.1979	0.8734	400	0.2390	0.4521	0.9043	0.9043	nan	0.9043	0.9043
0.2527	0.9825	450	0.2981	0.3835	0.7670	0.7670	nan	0.7670	0.7670
0.1658	1.0917	500	0.1473	0.4537	0.9073	0.9073	nan	0.9073	0.9073
0.1866	1.2009	550	0.2338	0.4246	0.8492	0.8492	nan	0.8492	0.8492
0.1665	1.3100	600	0.1739	0.4639	0.9278	0.9278	nan	0.9278	0.9278
0.1692	1.4192	650	0.1808	0.4511	0.9022	0.9022	nan	0.9022	0.9022
0.1803	1.5284	700	0.2468	0.4138	0.8277	0.8277	nan	0.8277	0.8277
0.1722	1.6376	750	0.1914	0.4345	0.8691	0.8691	nan	0.8691	0.8691
0.1526	1.7467	800	0.2183	0.4396	0.8792	0.8792	nan	0.8792	0.8792
0.1409	1.8559	850	0.2273	0.4216	0.8433	0.8433	nan	0.8433	0.8433
0.169	1.9651	900	0.2728	0.4036	0.8072	0.8072	nan	0.8072	0.8072
0.1302	2.0742	950	0.2208	0.4452	0.8903	0.8903	nan	0.8903	0.8903
0.1268	2.1834	1000	0.2283	0.4253	0.8507	0.8507	nan	0.8507	0.8507
0.1271	2.2926	1050	0.1984	0.4506	0.9012	0.9012	nan	0.9012	0.9012
0.1321	2.4017	1100	0.1618	0.4560	0.9120	0.9120	nan	0.9120	0.9120
0.1345	2.5109	1150	0.1725	0.4659	0.9318	0.9318	nan	0.9318	0.9318
0.1053	2.6201	1200	0.1550	0.4574	0.9148	0.9148	nan	0.9148	0.9148
0.1245	2.7293	1250	0.1696	0.4816	0.9632	0.9632	nan	0.9632	0.9632
0.1104	2.8384	1300	0.2519	0.4330	0.8661	0.8661	nan	0.8661	0.8661
0.1105	2.9476	1350	0.1830	0.4655	0.9310	0.9310	nan	0.9310	0.9310
0.1215	3.0568	1400	0.2102	0.4596	0.9192	0.9192	nan	0.9192	0.9192
0.0995	3.1659	1450	0.2363	0.4478	0.8957	0.8957	nan	0.8957	0.8957
0.1115	3.2751	1500	0.1730	0.4717	0.9435	0.9435	nan	0.9435	0.9435
0.0998	3.3843	1550	0.2067	0.4535	0.9070	0.9070	nan	0.9070	0.9070
0.0963	3.4934	1600	0.2127	0.4701	0.9401	0.9401	nan	0.9401	0.9401
0.0985	3.6026	1650	0.1695	0.4686	0.9371	0.9371	nan	0.9371	0.9371
0.0822	3.7118	1700	0.2069	0.4494	0.8988	0.8988	nan	0.8988	0.8988
0.1065	3.8210	1750	0.2140	0.4590	0.9179	0.9179	nan	0.9179	0.9179
0.0849	3.9301	1800	0.2108	0.4592	0.9183	0.9183	nan	0.9183	0.9183
0.0917	4.0393	1850	0.1940	0.4668	0.9336	0.9336	nan	0.9336	0.9336
0.0793	4.1485	1900	0.1795	0.4649	0.9298	0.9298	nan	0.9298	0.9298
0.0851	4.2576	1950	0.2118	0.4462	0.8924	0.8924	nan	0.8924	0.8924
0.0951	4.3668	2000	0.2864	0.4212	0.8424	0.8424	nan	0.8424	0.8424
0.0805	4.4760	2050	0.1498	0.4683	0.9366	0.9366	nan	0.9366	0.9366
0.085	4.5852	2100	0.2223	0.4514	0.9028	0.9028	nan	0.9028	0.9028
0.0736	4.6943	2150	0.1860	0.4695	0.9390	0.9390	nan	0.9390	0.9390
0.079	4.8035	2200	0.2069	0.4653	0.9305	0.9305	nan	0.9305	0.9305
0.0701	4.9127	2250	0.1728	0.4724	0.9448	0.9448	nan	0.9448	0.9448
0.0994	5.0218	2300	0.2480	0.4602	0.9204	0.9204	nan	0.9204	0.9204
0.0749	5.1310	2350	0.1951	0.4663	0.9325	0.9325	nan	0.9325	0.9325
0.0691	5.2402	2400	0.2103	0.4568	0.9136	0.9136	nan	0.9136	0.9136
0.0653	5.3493	2450	0.1794	0.4570	0.9140	0.9140	nan	0.9140	0.9140
0.0621	5.4585	2500	0.1971	0.4715	0.9430	0.9430	nan	0.9430	0.9430
0.073	5.5677	2550	0.1905	0.4589	0.9179	0.9179	nan	0.9179	0.9179
0.0658	5.6769	2600	0.2289	0.4791	0.9581	0.9581	nan	0.9581	0.9581
0.0727	5.7860	2650	0.1976	0.4769	0.9539	0.9539	nan	0.9539	0.9539
0.0756	5.8952	2700	0.1724	0.4687	0.9373	0.9373	nan	0.9373	0.9373
0.0756	6.0044	2750	0.1867	0.4566	0.9133	0.9133	nan	0.9133	0.9133
0.0695	6.1135	2800	0.1944	0.4715	0.9430	0.9430	nan	0.9430	0.9430
0.0683	6.2227	2850	0.2176	0.4744	0.9488	0.9488	nan	0.9488	0.9488
0.061	6.3319	2900	0.1959	0.4663	0.9326	0.9326	nan	0.9326	0.9326
0.06	6.4410	2950	0.2090	0.4615	0.9230	0.9230	nan	0.9230	0.9230
0.0537	6.5502	3000	0.2119	0.4735	0.9469	0.9469	nan	0.9469	0.9469
0.0529	6.6594	3050	0.2043	0.4568	0.9136	0.9136	nan	0.9136	0.9136
0.08	6.7686	3100	0.2130	0.4566	0.9132	0.9132	nan	0.9132	0.9132
0.0632	6.8777	3150	0.1993	0.4692	0.9384	0.9384	nan	0.9384	0.9384
0.0641	6.9869	3200	0.2408	0.4454	0.8909	0.8909	nan	0.8909	0.8909
0.0517	7.0961	3250	0.1836	0.4770	0.9540	0.9540	nan	0.9540	0.9540
0.0584	7.2052	3300	0.1983	0.4643	0.9285	0.9285	nan	0.9285	0.9285
0.0559	7.3144	3350	0.2036	0.4609	0.9217	0.9217	nan	0.9217	0.9217
0.0621	7.4236	3400	0.2058	0.4764	0.9528	0.9528	nan	0.9528	0.9528
0.0641	7.5328	3450	0.2136	0.4657	0.9314	0.9314	nan	0.9314	0.9314
0.0481	7.6419	3500	0.1938	0.4699	0.9398	0.9398	nan	0.9398	0.9398
0.061	7.7511	3550	0.1979	0.4772	0.9545	0.9545	nan	0.9545	0.9545
0.0561	7.8603	3600	0.2271	0.4691	0.9382	0.9382	nan	0.9382	0.9382
0.0629	7.9694	3650	0.2220	0.4596	0.9192	0.9192	nan	0.9192	0.9192
0.0625	8.0786	3700	0.2422	0.4547	0.9094	0.9094	nan	0.9094	0.9094
0.0479	8.1878	3750	0.2360	0.4791	0.9581	0.9581	nan	0.9581	0.9581
0.0471	8.2969	3800	0.1981	0.4713	0.9427	0.9427	nan	0.9427	0.9427
0.0612	8.4061	3850	0.2427	0.4740	0.9479	0.9479	nan	0.9479	0.9479
0.0526	8.5153	3900	0.2516	0.4716	0.9432	0.9432	nan	0.9432	0.9432
0.0573	8.6245	3950	0.2240	0.4663	0.9325	0.9325	nan	0.9325	0.9325
0.0532	8.7336	4000	0.2539	0.4830	0.9659	0.9659	nan	0.9659	0.9659
0.0537	8.8428	4050	0.2202	0.4633	0.9267	0.9267	nan	0.9267	0.9267
0.0481	8.9520	4100	0.2155	0.4617	0.9234	0.9234	nan	0.9234	0.9234
0.0461	9.0611	4150	0.2217	0.4590	0.9181	0.9181	nan	0.9181	0.9181
0.0486	9.1703	4200	0.2748	0.4420	0.8841	0.8841	nan	0.8841	0.8841
0.0485	9.2795	4250	0.2172	0.4680	0.9360	0.9360	nan	0.9360	0.9360
0.0559	9.3886	4300	0.2285	0.4717	0.9434	0.9434	nan	0.9434	0.9434
0.0434	9.4978	4350	0.2288	0.4749	0.9498	0.9498	nan	0.9498	0.9498
0.0522	9.6070	4400	0.2420	0.4609	0.9218	0.9218	nan	0.9218	0.9218
0.0453	9.7162	4450	0.2370	0.4741	0.9481	0.9481	nan	0.9481	0.9481
0.0538	9.8253	4500	0.2464	0.4565	0.9130	0.9130	nan	0.9130	0.9130
0.0513	9.9345	4550	0.2364	0.4754	0.9509	0.9509	nan	0.9509	0.9509

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu118
Datasets 3.5.0
Tokenizers 0.21.1

BigR-Oclock/segformer-b0-crop-detection

segformer-b0-crop-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BigR-Oclock/
segformer-b0-crop-detection