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dropoff-utcustom-train-SF-RGB-b5_1

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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-b5_1
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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-b5_1
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+
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+ This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6279
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+ - Mean Iou: 0.4054
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+ - Mean Accuracy: 0.7471
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+ - Overall Accuracy: 0.8860
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Dropoff: 0.5956
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+ - Accuracy Undropoff: 0.8986
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+ - Iou Unlabeled: 0.0
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+ - Iou Dropoff: 0.3318
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+ - Iou Undropoff: 0.8843
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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: 2e-06
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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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+ | 1.0071 | 5.0 | 10 | 1.0206 | 0.1745 | 0.2748 | 0.5034 | nan | 0.0255 | 0.5241 | 0.0 | 0.0147 | 0.5087 |
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+ | 0.9688 | 10.0 | 20 | 0.9873 | 0.2140 | 0.3486 | 0.5771 | nan | 0.0992 | 0.5979 | 0.0 | 0.0582 | 0.5838 |
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+ | 0.9406 | 15.0 | 30 | 0.9313 | 0.2613 | 0.4446 | 0.6655 | nan | 0.2038 | 0.6855 | 0.0 | 0.1135 | 0.6705 |
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+ | 0.9278 | 20.0 | 40 | 0.8851 | 0.2930 | 0.5149 | 0.7111 | nan | 0.3009 | 0.7289 | 0.0 | 0.1648 | 0.7142 |
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+ | 0.8956 | 25.0 | 50 | 0.8563 | 0.3118 | 0.5642 | 0.7358 | nan | 0.3770 | 0.7514 | 0.0 | 0.1985 | 0.7370 |
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+ | 0.8674 | 30.0 | 60 | 0.8260 | 0.3303 | 0.6086 | 0.7664 | nan | 0.4366 | 0.7807 | 0.0 | 0.2246 | 0.7664 |
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+ | 0.8438 | 35.0 | 70 | 0.8149 | 0.3347 | 0.6355 | 0.7671 | nan | 0.4921 | 0.7790 | 0.0 | 0.2381 | 0.7660 |
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+ | 0.8309 | 40.0 | 80 | 0.7881 | 0.3459 | 0.6472 | 0.7847 | nan | 0.4972 | 0.7972 | 0.0 | 0.2539 | 0.7839 |
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+ | 0.8069 | 45.0 | 90 | 0.7640 | 0.3567 | 0.6617 | 0.8041 | nan | 0.5063 | 0.8170 | 0.0 | 0.2668 | 0.8033 |
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+ | 0.7779 | 50.0 | 100 | 0.7486 | 0.3637 | 0.6792 | 0.8145 | nan | 0.5316 | 0.8268 | 0.0 | 0.2778 | 0.8132 |
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+ | 0.7695 | 55.0 | 110 | 0.7354 | 0.3684 | 0.6936 | 0.8214 | nan | 0.5542 | 0.8329 | 0.0 | 0.2858 | 0.8195 |
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+ | 0.7568 | 60.0 | 120 | 0.7164 | 0.3757 | 0.7032 | 0.8365 | nan | 0.5577 | 0.8486 | 0.0 | 0.2924 | 0.8347 |
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+ | 0.7285 | 65.0 | 130 | 0.6976 | 0.3836 | 0.7119 | 0.8484 | nan | 0.5630 | 0.8608 | 0.0 | 0.3042 | 0.8467 |
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+ | 0.7217 | 70.0 | 140 | 0.6922 | 0.3857 | 0.7217 | 0.8499 | nan | 0.5817 | 0.8616 | 0.0 | 0.3091 | 0.8480 |
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+ | 0.7095 | 75.0 | 150 | 0.6708 | 0.3926 | 0.7287 | 0.8624 | nan | 0.5828 | 0.8745 | 0.0 | 0.3172 | 0.8605 |
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+ | 0.6944 | 80.0 | 160 | 0.6637 | 0.3951 | 0.7320 | 0.8660 | nan | 0.5858 | 0.8781 | 0.0 | 0.3212 | 0.8641 |
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+ | 0.6878 | 85.0 | 170 | 0.6632 | 0.3942 | 0.7397 | 0.8673 | nan | 0.6005 | 0.8788 | 0.0 | 0.3175 | 0.8652 |
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+ | 0.6868 | 90.0 | 180 | 0.6468 | 0.3998 | 0.7391 | 0.8756 | nan | 0.5902 | 0.8880 | 0.0 | 0.3257 | 0.8739 |
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+ | 0.6581 | 95.0 | 190 | 0.6444 | 0.4003 | 0.7421 | 0.8776 | nan | 0.5942 | 0.8899 | 0.0 | 0.3249 | 0.8759 |
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+ | 0.6587 | 100.0 | 200 | 0.6383 | 0.4026 | 0.7427 | 0.8814 | nan | 0.5914 | 0.8940 | 0.0 | 0.3281 | 0.8797 |
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+ | 0.6525 | 105.0 | 210 | 0.6334 | 0.4032 | 0.7434 | 0.8825 | nan | 0.5918 | 0.8951 | 0.0 | 0.3289 | 0.8808 |
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+ | 0.658 | 110.0 | 220 | 0.6345 | 0.4026 | 0.7451 | 0.8811 | nan | 0.5968 | 0.8934 | 0.0 | 0.3285 | 0.8793 |
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+ | 0.6575 | 115.0 | 230 | 0.6300 | 0.4050 | 0.7463 | 0.8851 | nan | 0.5948 | 0.8977 | 0.0 | 0.3314 | 0.8835 |
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+ | 0.6625 | 120.0 | 240 | 0.6279 | 0.4054 | 0.7471 | 0.8860 | nan | 0.5956 | 0.8986 | 0.0 | 0.3318 | 0.8843 |
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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