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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-RGBD-b5_7 |
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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-RGBD-b5_7 |
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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: 0.1296 |
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- Mean Iou: 0.6242 |
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- Mean Accuracy: 0.6623 |
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- Overall Accuracy: 0.9652 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.3319 |
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- Accuracy Undropoff: 0.9926 |
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- Iou Unlabeled: nan |
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- Iou Dropoff: 0.2838 |
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- Iou Undropoff: 0.9647 |
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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-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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### 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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| 0.9278 | 5.0 | 10 | 0.8454 | 0.3197 | 0.5545 | 0.8788 | nan | 0.2009 | 0.9082 | 0.0 | 0.0807 | 0.8785 | |
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| 0.5551 | 10.0 | 20 | 0.4668 | 0.3221 | 0.5042 | 0.9540 | nan | 0.0135 | 0.9948 | 0.0 | 0.0122 | 0.9540 | |
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| 0.3667 | 15.0 | 30 | 0.3354 | 0.3218 | 0.5035 | 0.9570 | nan | 0.0088 | 0.9982 | 0.0 | 0.0085 | 0.9570 | |
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| 0.2402 | 20.0 | 40 | 0.2678 | 0.5985 | 0.6492 | 0.9587 | nan | 0.3116 | 0.9868 | nan | 0.2388 | 0.9582 | |
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| 0.1562 | 25.0 | 50 | 0.2101 | 0.6240 | 0.6719 | 0.9631 | nan | 0.3544 | 0.9895 | nan | 0.2854 | 0.9625 | |
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| 0.1159 | 30.0 | 60 | 0.1704 | 0.6262 | 0.6641 | 0.9654 | nan | 0.3353 | 0.9928 | nan | 0.2875 | 0.9650 | |
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| 0.0869 | 35.0 | 70 | 0.1443 | 0.6380 | 0.6817 | 0.9657 | nan | 0.3720 | 0.9915 | nan | 0.3108 | 0.9652 | |
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| 0.079 | 40.0 | 80 | 0.1350 | 0.6072 | 0.6360 | 0.9654 | nan | 0.2766 | 0.9953 | nan | 0.2494 | 0.9650 | |
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| 0.0647 | 45.0 | 90 | 0.1370 | 0.5800 | 0.6031 | 0.9643 | nan | 0.2090 | 0.9971 | nan | 0.1959 | 0.9640 | |
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| 0.0587 | 50.0 | 100 | 0.1336 | 0.6276 | 0.6796 | 0.9628 | nan | 0.3707 | 0.9885 | nan | 0.2929 | 0.9622 | |
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| 0.0575 | 55.0 | 110 | 0.1313 | 0.6189 | 0.6531 | 0.9654 | nan | 0.3126 | 0.9937 | nan | 0.2729 | 0.9649 | |
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| 0.0527 | 60.0 | 120 | 0.1298 | 0.6252 | 0.6655 | 0.9648 | nan | 0.3391 | 0.9920 | nan | 0.2860 | 0.9643 | |
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| 0.0491 | 65.0 | 130 | 0.1313 | 0.6110 | 0.6492 | 0.9635 | nan | 0.3063 | 0.9920 | nan | 0.2589 | 0.9631 | |
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| 0.0441 | 70.0 | 140 | 0.1295 | 0.6103 | 0.6429 | 0.9648 | nan | 0.2919 | 0.9939 | nan | 0.2562 | 0.9643 | |
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| 0.0426 | 75.0 | 150 | 0.1233 | 0.6271 | 0.6633 | 0.9659 | nan | 0.3333 | 0.9933 | nan | 0.2887 | 0.9654 | |
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| 0.0477 | 80.0 | 160 | 0.1286 | 0.6255 | 0.6629 | 0.9655 | nan | 0.3328 | 0.9929 | nan | 0.2861 | 0.9650 | |
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| 0.039 | 85.0 | 170 | 0.1265 | 0.6380 | 0.6824 | 0.9656 | nan | 0.3735 | 0.9913 | nan | 0.3109 | 0.9650 | |
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| 0.0378 | 90.0 | 180 | 0.1309 | 0.6185 | 0.6543 | 0.9650 | nan | 0.3154 | 0.9932 | nan | 0.2725 | 0.9645 | |
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| 0.0362 | 95.0 | 190 | 0.1266 | 0.6311 | 0.6715 | 0.9655 | nan | 0.3508 | 0.9922 | nan | 0.2973 | 0.9650 | |
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| 0.0394 | 100.0 | 200 | 0.1307 | 0.6274 | 0.6635 | 0.9659 | nan | 0.3337 | 0.9934 | nan | 0.2894 | 0.9655 | |
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| 0.0362 | 105.0 | 210 | 0.1271 | 0.6366 | 0.6789 | 0.9658 | nan | 0.3661 | 0.9918 | nan | 0.3080 | 0.9653 | |
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| 0.0361 | 110.0 | 220 | 0.1274 | 0.6317 | 0.6736 | 0.9653 | nan | 0.3554 | 0.9918 | nan | 0.2987 | 0.9648 | |
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| 0.0353 | 115.0 | 230 | 0.1290 | 0.6216 | 0.6579 | 0.9652 | nan | 0.3228 | 0.9931 | nan | 0.2784 | 0.9647 | |
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| 0.0344 | 120.0 | 240 | 0.1296 | 0.6242 | 0.6623 | 0.9652 | nan | 0.3319 | 0.9926 | nan | 0.2838 | 0.9647 | |
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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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