End of training

a8d0312 verified 27 days ago

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	---
	library_name: transformers
	license: other
	base_model: nvidia/mit-b0
	tags:
	- image-segmentation
	- vision
	- generated_from_trainer
	datasets:
	- generator
	model-index:
	- name: autocrop-av-abm
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# autocrop-av-abm

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/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

	---
	library_name: transformers
	license: other
	base_model: nvidia/mit-b0
	tags:
	- image-segmentation
	- vision
	- generated_from_trainer
	datasets:
	- generator
	model-index:
	- name: autocrop-av-abm
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# autocrop-av-abm

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/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