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--- |
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license: other |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: dropoff-utcustom-train-SF-RGB-b5_3 |
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results: [] |
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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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# dropoff-utcustom-train-SF-RGB-b5_3 |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3770 |
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- Mean Iou: 0.4572 |
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- Mean Accuracy: 0.7822 |
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- Overall Accuracy: 0.9640 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.5839 |
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- Accuracy Undropoff: 0.9805 |
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- Iou Unlabeled: 0.0 |
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- Iou Dropoff: 0.4086 |
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- Iou Undropoff: 0.9631 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 15 |
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- eval_batch_size: 15 |
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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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### Training results |
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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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| 1.3135 | 5.0 | 10 | 1.2008 | 0.0546 | 0.2586 | 0.1227 | nan | 0.4069 | 0.1103 | 0.0 | 0.0535 | 0.1102 | |
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| 1.2309 | 10.0 | 20 | 1.1294 | 0.1176 | 0.3397 | 0.2490 | nan | 0.4388 | 0.2407 | 0.0 | 0.1129 | 0.2400 | |
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| 1.1346 | 15.0 | 30 | 1.0395 | 0.2171 | 0.4865 | 0.5022 | nan | 0.4694 | 0.5036 | 0.0 | 0.1524 | 0.4989 | |
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| 1.1088 | 20.0 | 40 | 0.9755 | 0.2608 | 0.5521 | 0.6176 | nan | 0.4808 | 0.6235 | 0.0 | 0.1661 | 0.6163 | |
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| 1.007 | 25.0 | 50 | 0.9197 | 0.2895 | 0.5959 | 0.6775 | nan | 0.5068 | 0.6849 | 0.0 | 0.1923 | 0.6763 | |
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| 0.9145 | 30.0 | 60 | 0.8635 | 0.3162 | 0.6299 | 0.7335 | nan | 0.5168 | 0.7429 | 0.0 | 0.2156 | 0.7329 | |
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| 0.8745 | 35.0 | 70 | 0.8070 | 0.3398 | 0.6784 | 0.7808 | nan | 0.5667 | 0.7901 | 0.0 | 0.2404 | 0.7791 | |
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| 0.8088 | 40.0 | 80 | 0.7442 | 0.3667 | 0.7191 | 0.8290 | nan | 0.5993 | 0.8389 | 0.0 | 0.2730 | 0.8272 | |
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| 0.7184 | 45.0 | 90 | 0.6956 | 0.3832 | 0.7513 | 0.8603 | nan | 0.6323 | 0.8702 | 0.0 | 0.2915 | 0.8580 | |
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| 0.6908 | 50.0 | 100 | 0.6751 | 0.3931 | 0.7592 | 0.8748 | nan | 0.6332 | 0.8853 | 0.0 | 0.3067 | 0.8728 | |
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| 0.643 | 55.0 | 110 | 0.6101 | 0.4134 | 0.7714 | 0.9108 | nan | 0.6194 | 0.9234 | 0.0 | 0.3308 | 0.9094 | |
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| 0.6014 | 60.0 | 120 | 0.5971 | 0.4166 | 0.7826 | 0.9189 | nan | 0.6339 | 0.9313 | 0.0 | 0.3324 | 0.9175 | |
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| 0.5685 | 65.0 | 130 | 0.5595 | 0.4304 | 0.7946 | 0.9328 | nan | 0.6439 | 0.9453 | 0.0 | 0.3599 | 0.9314 | |
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| 0.5172 | 70.0 | 140 | 0.5344 | 0.4373 | 0.8010 | 0.9406 | nan | 0.6488 | 0.9532 | 0.0 | 0.3727 | 0.9393 | |
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| 0.4757 | 75.0 | 150 | 0.4963 | 0.4434 | 0.7997 | 0.9490 | nan | 0.6368 | 0.9626 | 0.0 | 0.3822 | 0.9479 | |
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| 0.4288 | 80.0 | 160 | 0.4599 | 0.4488 | 0.7936 | 0.9556 | nan | 0.6169 | 0.9702 | 0.0 | 0.3918 | 0.9546 | |
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| 0.4124 | 85.0 | 170 | 0.4710 | 0.4469 | 0.7989 | 0.9540 | nan | 0.6296 | 0.9681 | 0.0 | 0.3876 | 0.9529 | |
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| 0.4995 | 90.0 | 180 | 0.4209 | 0.4537 | 0.7883 | 0.9606 | nan | 0.6004 | 0.9762 | 0.0 | 0.4015 | 0.9597 | |
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| 0.3815 | 95.0 | 190 | 0.4287 | 0.4524 | 0.7919 | 0.9595 | nan | 0.6090 | 0.9748 | 0.0 | 0.3988 | 0.9586 | |
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| 0.3764 | 100.0 | 200 | 0.4245 | 0.4529 | 0.7913 | 0.9600 | nan | 0.6073 | 0.9753 | 0.0 | 0.3998 | 0.9590 | |
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| 0.4074 | 105.0 | 210 | 0.4096 | 0.4542 | 0.7894 | 0.9613 | nan | 0.6018 | 0.9769 | 0.0 | 0.4021 | 0.9603 | |
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| 0.3975 | 110.0 | 220 | 0.4107 | 0.4538 | 0.7905 | 0.9610 | nan | 0.6045 | 0.9765 | 0.0 | 0.4013 | 0.9601 | |
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| 0.3598 | 115.0 | 230 | 0.3918 | 0.4558 | 0.7863 | 0.9627 | nan | 0.5939 | 0.9787 | 0.0 | 0.4057 | 0.9618 | |
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| 0.3709 | 120.0 | 240 | 0.3770 | 0.4572 | 0.7822 | 0.9640 | nan | 0.5839 | 0.9805 | 0.0 | 0.4086 | 0.9631 | |
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### Framework versions |
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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 |
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