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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_6 |
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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_6 |
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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: 0.1429 |
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- Mean Iou: 0.6443 |
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- Mean Accuracy: 0.6853 |
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- Overall Accuracy: 0.9669 |
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- Accuracy Unlabeled: nan |
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- Accuracy Dropoff: 0.3782 |
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- Accuracy Undropoff: 0.9925 |
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- Iou Unlabeled: nan |
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- Iou Dropoff: 0.3223 |
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- Iou Undropoff: 0.9664 |
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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: 2e-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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| 1.159 | 5.0 | 10 | 1.0040 | 0.2283 | 0.5676 | 0.6267 | nan | 0.5031 | 0.6321 | 0.0 | 0.0644 | 0.6203 | |
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| 0.8345 | 10.0 | 20 | 0.7480 | 0.3236 | 0.5320 | 0.9158 | nan | 0.1134 | 0.9506 | 0.0 | 0.0555 | 0.9154 | |
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| 0.5406 | 15.0 | 30 | 0.5477 | 0.3223 | 0.5049 | 0.9513 | nan | 0.0179 | 0.9918 | 0.0 | 0.0157 | 0.9513 | |
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| 0.3695 | 20.0 | 40 | 0.4590 | 0.3215 | 0.5036 | 0.9519 | nan | 0.0146 | 0.9926 | 0.0 | 0.0125 | 0.9519 | |
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| 0.3053 | 25.0 | 50 | 0.3790 | 0.3196 | 0.5001 | 0.9565 | nan | 0.0023 | 0.9979 | 0.0 | 0.0022 | 0.9565 | |
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| 0.2436 | 30.0 | 60 | 0.3303 | 0.4812 | 0.5020 | 0.9568 | nan | 0.0059 | 0.9981 | nan | 0.0056 | 0.9568 | |
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| 0.2148 | 35.0 | 70 | 0.2739 | 0.4794 | 0.5002 | 0.9580 | nan | 0.0008 | 0.9996 | nan | 0.0008 | 0.9580 | |
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| 0.1983 | 40.0 | 80 | 0.2348 | 0.5079 | 0.5284 | 0.9595 | nan | 0.0582 | 0.9986 | nan | 0.0564 | 0.9594 | |
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| 0.1784 | 45.0 | 90 | 0.2178 | 0.6064 | 0.6440 | 0.9631 | nan | 0.2960 | 0.9920 | nan | 0.2501 | 0.9626 | |
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| 0.1631 | 50.0 | 100 | 0.1943 | 0.6223 | 0.6811 | 0.9607 | nan | 0.3760 | 0.9861 | nan | 0.2846 | 0.9601 | |
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| 0.1468 | 55.0 | 110 | 0.1759 | 0.6206 | 0.6731 | 0.9617 | nan | 0.3583 | 0.9879 | nan | 0.2801 | 0.9611 | |
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| 0.1353 | 60.0 | 120 | 0.1657 | 0.6014 | 0.6335 | 0.9639 | nan | 0.2731 | 0.9939 | nan | 0.2393 | 0.9635 | |
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| 0.1474 | 65.0 | 130 | 0.1590 | 0.5943 | 0.6228 | 0.9641 | nan | 0.2505 | 0.9951 | nan | 0.2249 | 0.9637 | |
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| 0.1172 | 70.0 | 140 | 0.1562 | 0.6272 | 0.6662 | 0.9653 | nan | 0.3400 | 0.9924 | nan | 0.2896 | 0.9648 | |
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| 0.1169 | 75.0 | 150 | 0.1538 | 0.6302 | 0.6696 | 0.9656 | nan | 0.3467 | 0.9925 | nan | 0.2954 | 0.9651 | |
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| 0.1263 | 80.0 | 160 | 0.1540 | 0.6372 | 0.6784 | 0.9661 | nan | 0.3645 | 0.9922 | nan | 0.3089 | 0.9656 | |
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| 0.1028 | 85.0 | 170 | 0.1512 | 0.6462 | 0.6948 | 0.9659 | nan | 0.3992 | 0.9904 | nan | 0.3271 | 0.9653 | |
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| 0.1163 | 90.0 | 180 | 0.1493 | 0.6469 | 0.6932 | 0.9663 | nan | 0.3953 | 0.9911 | nan | 0.3280 | 0.9658 | |
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| 0.0998 | 95.0 | 190 | 0.1481 | 0.6457 | 0.6894 | 0.9666 | nan | 0.3869 | 0.9918 | nan | 0.3253 | 0.9661 | |
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| 0.0997 | 100.0 | 200 | 0.1465 | 0.6454 | 0.6893 | 0.9665 | nan | 0.3869 | 0.9917 | nan | 0.3247 | 0.9660 | |
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| 0.0998 | 105.0 | 210 | 0.1473 | 0.6488 | 0.6937 | 0.9668 | nan | 0.3958 | 0.9916 | nan | 0.3313 | 0.9662 | |
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| 0.1003 | 110.0 | 220 | 0.1437 | 0.6401 | 0.6774 | 0.9671 | nan | 0.3614 | 0.9934 | nan | 0.3136 | 0.9666 | |
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| 0.0932 | 115.0 | 230 | 0.1434 | 0.6469 | 0.6898 | 0.9669 | nan | 0.3876 | 0.9920 | nan | 0.3275 | 0.9664 | |
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| 0.0942 | 120.0 | 240 | 0.1429 | 0.6443 | 0.6853 | 0.9669 | nan | 0.3782 | 0.9925 | nan | 0.3223 | 0.9664 | |
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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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