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dropoff-utcustom-train-SF-RGB-b0_7

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+ ---
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+ license: other
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: dropoff-utcustom-train-SF-RGB-b0_7
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # dropoff-utcustom-train-SF-RGB-b0_7
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1457
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+ - Mean Iou: 0.6795
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+ - Mean Accuracy: 0.7207
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+ - Overall Accuracy: 0.9691
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Dropoff: 0.4481
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+ - Accuracy Undropoff: 0.9932
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+ - Iou Unlabeled: nan
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+ - Iou Dropoff: 0.3907
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+ - Iou Undropoff: 0.9684
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 9e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 120
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
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+ | 1.1505 | 3.33 | 10 | 1.1103 | 0.1106 | 0.6036 | 0.2919 | nan | 0.9456 | 0.2616 | 0.0 | 0.0703 | 0.2616 |
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+ | 0.9635 | 6.67 | 20 | 1.0114 | 0.3710 | 0.8470 | 0.8737 | nan | 0.8177 | 0.8763 | 0.0 | 0.2435 | 0.8694 |
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+ | 0.9358 | 10.0 | 30 | 0.8242 | 0.4206 | 0.7727 | 0.9440 | nan | 0.5848 | 0.9606 | 0.0 | 0.3194 | 0.9425 |
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+ | 0.579 | 13.33 | 40 | 0.5703 | 0.4525 | 0.7615 | 0.9633 | nan | 0.5402 | 0.9829 | 0.0 | 0.3951 | 0.9624 |
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+ | 0.4411 | 16.67 | 50 | 0.4166 | 0.4529 | 0.7380 | 0.9667 | nan | 0.4872 | 0.9889 | 0.0 | 0.3928 | 0.9659 |
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+ | 0.4311 | 20.0 | 60 | 0.3843 | 0.6678 | 0.7156 | 0.9667 | nan | 0.4400 | 0.9911 | nan | 0.3695 | 0.9661 |
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+ | 0.3437 | 23.33 | 70 | 0.3590 | 0.4347 | 0.6956 | 0.9655 | nan | 0.3995 | 0.9918 | 0.0 | 0.3392 | 0.9649 |
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+ | 0.3136 | 26.67 | 80 | 0.3198 | 0.6259 | 0.6622 | 0.9638 | nan | 0.3312 | 0.9931 | nan | 0.2885 | 0.9633 |
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+ | 0.2682 | 30.0 | 90 | 0.2919 | 0.6187 | 0.6470 | 0.9648 | nan | 0.2984 | 0.9957 | nan | 0.2730 | 0.9643 |
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+ | 0.2521 | 33.33 | 100 | 0.2957 | 0.6448 | 0.6845 | 0.9653 | nan | 0.3764 | 0.9926 | nan | 0.3248 | 0.9648 |
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+ | 0.2287 | 36.67 | 110 | 0.2747 | 0.6800 | 0.7256 | 0.9685 | nan | 0.4591 | 0.9921 | nan | 0.3922 | 0.9678 |
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+ | 0.2203 | 40.0 | 120 | 0.2537 | 0.7108 | 0.7687 | 0.9706 | nan | 0.5472 | 0.9902 | nan | 0.4517 | 0.9699 |
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+ | 0.1964 | 43.33 | 130 | 0.2356 | 0.6689 | 0.7054 | 0.9686 | nan | 0.4167 | 0.9941 | nan | 0.3699 | 0.9680 |
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+ | 0.1776 | 46.67 | 140 | 0.2205 | 0.6729 | 0.7137 | 0.9684 | nan | 0.4343 | 0.9931 | nan | 0.3780 | 0.9677 |
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+ | 0.1675 | 50.0 | 150 | 0.2061 | 0.6809 | 0.7244 | 0.9689 | nan | 0.4562 | 0.9926 | nan | 0.3936 | 0.9682 |
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+ | 0.148 | 53.33 | 160 | 0.1954 | 0.6924 | 0.7418 | 0.9694 | nan | 0.4920 | 0.9915 | nan | 0.4160 | 0.9687 |
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+ | 0.1364 | 56.67 | 170 | 0.1915 | 0.6869 | 0.7415 | 0.9681 | nan | 0.4928 | 0.9902 | nan | 0.4064 | 0.9674 |
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+ | 0.1171 | 60.0 | 180 | 0.1776 | 0.7206 | 0.7816 | 0.9714 | nan | 0.5734 | 0.9899 | nan | 0.4706 | 0.9707 |
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+ | 0.1169 | 63.33 | 190 | 0.1754 | 0.6580 | 0.6853 | 0.9689 | nan | 0.3741 | 0.9965 | nan | 0.3476 | 0.9684 |
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+ | 0.1178 | 66.67 | 200 | 0.1676 | 0.6783 | 0.7233 | 0.9684 | nan | 0.4545 | 0.9922 | nan | 0.3888 | 0.9677 |
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+ | 0.1016 | 70.0 | 210 | 0.1670 | 0.6633 | 0.6985 | 0.9682 | nan | 0.4025 | 0.9944 | nan | 0.3590 | 0.9676 |
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+ | 0.1025 | 73.33 | 220 | 0.1648 | 0.6789 | 0.7154 | 0.9696 | nan | 0.4366 | 0.9943 | nan | 0.3888 | 0.9690 |
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+ | 0.0956 | 76.67 | 230 | 0.1607 | 0.6684 | 0.7103 | 0.9677 | nan | 0.4279 | 0.9927 | nan | 0.3697 | 0.9671 |
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+ | 0.1443 | 80.0 | 240 | 0.1611 | 0.6747 | 0.7134 | 0.9688 | nan | 0.4332 | 0.9937 | nan | 0.3811 | 0.9682 |
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+ | 0.0902 | 83.33 | 250 | 0.1600 | 0.6713 | 0.7060 | 0.9691 | nan | 0.4174 | 0.9946 | nan | 0.3740 | 0.9685 |
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+ | 0.0846 | 86.67 | 260 | 0.1559 | 0.6772 | 0.7263 | 0.9677 | nan | 0.4613 | 0.9912 | nan | 0.3874 | 0.9670 |
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+ | 0.1166 | 90.0 | 270 | 0.1587 | 0.6615 | 0.6984 | 0.9677 | nan | 0.4030 | 0.9939 | nan | 0.3559 | 0.9671 |
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+ | 0.0825 | 93.33 | 280 | 0.1538 | 0.6684 | 0.7068 | 0.9682 | nan | 0.4199 | 0.9936 | nan | 0.3692 | 0.9676 |
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+ | 0.0769 | 96.67 | 290 | 0.1527 | 0.6649 | 0.7033 | 0.9679 | nan | 0.4130 | 0.9936 | nan | 0.3626 | 0.9673 |
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+ | 0.0722 | 100.0 | 300 | 0.1473 | 0.6832 | 0.7247 | 0.9694 | nan | 0.4563 | 0.9932 | nan | 0.3976 | 0.9688 |
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+ | 0.0779 | 103.33 | 310 | 0.1465 | 0.6809 | 0.7200 | 0.9695 | nan | 0.4462 | 0.9937 | nan | 0.3930 | 0.9689 |
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+ | 0.0771 | 106.67 | 320 | 0.1494 | 0.6673 | 0.7052 | 0.9682 | nan | 0.4167 | 0.9937 | nan | 0.3670 | 0.9676 |
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+ | 0.1082 | 110.0 | 330 | 0.1479 | 0.6753 | 0.7182 | 0.9683 | nan | 0.4438 | 0.9926 | nan | 0.3830 | 0.9677 |
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+ | 0.0726 | 113.33 | 340 | 0.1451 | 0.6765 | 0.7159 | 0.9689 | nan | 0.4384 | 0.9935 | nan | 0.3846 | 0.9683 |
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+ | 0.0743 | 116.67 | 350 | 0.1469 | 0.6814 | 0.7249 | 0.9689 | nan | 0.4571 | 0.9927 | nan | 0.3946 | 0.9683 |
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+ | 0.0703 | 120.0 | 360 | 0.1457 | 0.6795 | 0.7207 | 0.9691 | nan | 0.4481 | 0.9932 | nan | 0.3907 | 0.9684 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3