autocrop-av-abm

This model is a fine-tuned version of nvidia/mit-b0 on the /mnt/disk1/autocrop-data/datasets/av-abm dataset. It achieves the following results on the evaluation set:

• Loss: 0.0146
• Mean Iou: 0.4940
• Mean Accuracy: 0.9880
• Overall Accuracy: 0.9880
• Accuracy Background: nan
• Accuracy Crop: 0.9880
• Iou Background: 0.0
• Iou Crop: 0.9880

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Background	Accuracy Crop	Iou Background	Iou Crop
0.1919	1.0	225	0.1796	0.4930	0.9859	0.9859	nan	0.9859	0.0	0.9859
0.1073	2.0	450	0.1057	0.4895	0.9790	0.9790	nan	0.9790	0.0	0.9790
0.0572	3.0	675	0.0490	0.4853	0.9706	0.9706	nan	0.9706	0.0	0.9706
0.0375	4.0	900	0.0371	0.4902	0.9804	0.9804	nan	0.9804	0.0	0.9804
0.0440	5.0	1125	0.0316	0.4919	0.9837	0.9837	nan	0.9837	0.0	0.9837
0.0228	6.0	1350	0.0250	0.4902	0.9803	0.9803	nan	0.9803	0.0	0.9803
0.0294	7.0	1575	0.0233	0.4873	0.9746	0.9746	nan	0.9746	0.0	0.9746
0.0213	8.0	1800	0.0209	0.4931	0.9863	0.9863	nan	0.9863	0.0	0.9863
0.0215	9.0	2025	0.0220	0.4940	0.9881	0.9881	nan	0.9881	0.0	0.9881
0.0186	10.0	2250	0.0180	0.4924	0.9849	0.9849	nan	0.9849	0.0	0.9849
0.0195	11.0	2475	0.0186	0.4927	0.9855	0.9855	nan	0.9855	0.0	0.9855
0.0136	12.0	2700	0.0174	0.4917	0.9835	0.9835	nan	0.9835	0.0	0.9835
0.0197	13.0	2925	0.0182	0.4949	0.9898	0.9898	nan	0.9898	0.0	0.9898
0.0141	14.0	3150	0.0164	0.4931	0.9861	0.9861	nan	0.9861	0.0	0.9861
0.0138	15.0	3375	0.0169	0.4917	0.9835	0.9835	nan	0.9835	0.0	0.9835
0.0123	16.0	3600	0.0175	0.4926	0.9852	0.9852	nan	0.9852	0.0	0.9852
0.0171	17.0	3825	0.0166	0.4920	0.9839	0.9839	nan	0.9839	0.0	0.9839
0.0135	18.0	4050	0.0159	0.4930	0.9860	0.9860	nan	0.9860	0.0	0.9860
0.0155	19.0	4275	0.0151	0.4931	0.9862	0.9862	nan	0.9862	0.0	0.9862
0.0141	20.0	4500	0.0151	0.4936	0.9871	0.9871	nan	0.9871	0.0	0.9871
0.0174	21.0	4725	0.0151	0.4935	0.9870	0.9870	nan	0.9870	0.0	0.9870
0.0125	22.0	4950	0.0155	0.4936	0.9871	0.9871	nan	0.9871	0.0	0.9871
0.0115	23.0	5175	0.0157	0.4926	0.9852	0.9852	nan	0.9852	0.0	0.9852
0.0124	24.0	5400	0.0156	0.4933	0.9866	0.9866	nan	0.9866	0.0	0.9866
0.0122	25.0	5625	0.0149	0.4934	0.9867	0.9867	nan	0.9867	0.0	0.9867
0.0116	26.0	5850	0.0164	0.4922	0.9844	0.9844	nan	0.9844	0.0	0.9844
0.0122	27.0	6075	0.0146	0.4940	0.9880	0.9880	nan	0.9880	0.0	0.9880
0.0085	28.0	6300	0.0161	0.4932	0.9864	0.9864	nan	0.9864	0.0	0.9864
0.0122	29.0	6525	0.0151	0.4925	0.9851	0.9851	nan	0.9851	0.0	0.9851
0.0115	30.0	6750	0.0165	0.4930	0.9861	0.9861	nan	0.9861	0.0	0.9861
0.0117	31.0	6975	0.0160	0.4924	0.9848	0.9848	nan	0.9848	0.0	0.9848
0.0105	32.0	7200	0.0161	0.4941	0.9883	0.9883	nan	0.9883	0.0	0.9883
0.0113	33.0	7425	0.0155	0.4930	0.9859	0.9859	nan	0.9859	0.0	0.9859
0.0089	34.0	7650	0.0161	0.4937	0.9873	0.9873	nan	0.9873	0.0	0.9873
0.0114	35.0	7875	0.0157	0.4939	0.9877	0.9877	nan	0.9877	0.0	0.9877
0.0094	36.0	8100	0.0161	0.4943	0.9887	0.9887	nan	0.9887	0.0	0.9887
0.0082	37.0	8325	0.0162	0.4936	0.9871	0.9871	nan	0.9871	0.0	0.9871
0.0081	38.0	8550	0.0161	0.4927	0.9853	0.9853	nan	0.9853	0.0	0.9853
0.0090	39.0	8775	0.0169	0.4931	0.9862	0.9862	nan	0.9862	0.0	0.9862
0.0084	40.0	9000	0.0161	0.4934	0.9868	0.9868	nan	0.9868	0.0	0.9868
0.0115	41.0	9225	0.0158	0.4935	0.9870	0.9870	nan	0.9870	0.0	0.9870
0.0080	42.0	9450	0.0162	0.4935	0.9870	0.9870	nan	0.9870	0.0	0.9870
0.0097	43.0	9675	0.0167	0.4937	0.9874	0.9874	nan	0.9874	0.0	0.9874
0.0082	44.0	9900	0.0166	0.4934	0.9867	0.9867	nan	0.9867	0.0	0.9867
0.0097	45.0	10125	0.0168	0.4936	0.9872	0.9872	nan	0.9872	0.0	0.9872
0.0076	46.0	10350	0.0167	0.4933	0.9866	0.9866	nan	0.9866	0.0	0.9866
0.0085	47.0	10575	0.0164	0.4936	0.9873	0.9873	nan	0.9873	0.0	0.9873
0.0090	48.0	10800	0.0164	0.4935	0.9870	0.9870	nan	0.9870	0.0	0.9870
0.0081	49.0	11025	0.0164	0.4935	0.9869	0.9869	nan	0.9869	0.0	0.9869
0.0099	50.0	11250	0.0165	0.4935	0.9869	0.9869	nan	0.9869	0.0	0.9869

Framework versions

• Transformers 5.8.0
• Pytorch 2.11.0+cu130
• Datasets 4.8.5
• Tokenizers 0.22.2