safety-utcustom-train-SF30-RGBD-b5
This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN-30 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1952
- Mean Iou: 0.6486
- Mean Accuracy: 0.7199
- Overall Accuracy: 0.9704
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.4523
- Accuracy Unsafe: 0.9874
- Iou Unlabeled: nan
- Iou Safe: 0.3271
- Iou Unsafe: 0.9700
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
Training results
Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.8758 | 5.0 | 10 | 0.9831 | 0.3415 | 0.6100 | 0.9154 | nan | 0.2839 | 0.9362 | 0.0 | 0.1099 | 0.9147 |
0.7637 | 10.0 | 20 | 0.7236 | 0.3771 | 0.6275 | 0.9582 | nan | 0.2745 | 0.9806 | 0.0 | 0.1735 | 0.9578 |
0.6698 | 15.0 | 30 | 0.5510 | 0.3789 | 0.6286 | 0.9593 | nan | 0.2755 | 0.9818 | 0.0 | 0.1776 | 0.9590 |
0.5935 | 20.0 | 40 | 0.4632 | 0.3822 | 0.6388 | 0.9591 | nan | 0.2967 | 0.9809 | 0.0 | 0.1877 | 0.9588 |
0.5108 | 25.0 | 50 | 0.4239 | 0.3814 | 0.6492 | 0.9560 | nan | 0.3214 | 0.9769 | 0.0 | 0.1887 | 0.9556 |
0.4597 | 30.0 | 60 | 0.4134 | 0.3845 | 0.6422 | 0.9596 | nan | 0.3034 | 0.9811 | 0.0 | 0.1943 | 0.9592 |
0.4307 | 35.0 | 70 | 0.3918 | 0.3900 | 0.6516 | 0.9594 | nan | 0.3229 | 0.9803 | 0.0 | 0.2111 | 0.9590 |
0.367 | 40.0 | 80 | 0.3578 | 0.3885 | 0.6600 | 0.9582 | nan | 0.3415 | 0.9784 | 0.0 | 0.2077 | 0.9577 |
0.3249 | 45.0 | 90 | 0.3395 | 0.3921 | 0.6587 | 0.9607 | nan | 0.3360 | 0.9813 | 0.0 | 0.2161 | 0.9603 |
0.292 | 50.0 | 100 | 0.3124 | 0.3969 | 0.6622 | 0.9633 | nan | 0.3408 | 0.9837 | 0.0 | 0.2280 | 0.9629 |
0.2766 | 55.0 | 110 | 0.2820 | 0.4078 | 0.6878 | 0.9644 | nan | 0.3925 | 0.9831 | 0.0 | 0.2594 | 0.9639 |
0.2347 | 60.0 | 120 | 0.2673 | 0.6169 | 0.7000 | 0.9641 | nan | 0.4181 | 0.9820 | nan | 0.2701 | 0.9636 |
0.226 | 65.0 | 130 | 0.2350 | 0.6280 | 0.6854 | 0.9698 | nan | 0.3818 | 0.9891 | nan | 0.2865 | 0.9694 |
0.3262 | 70.0 | 140 | 0.2354 | 0.6338 | 0.7125 | 0.9674 | nan | 0.4402 | 0.9848 | nan | 0.3006 | 0.9670 |
0.1991 | 75.0 | 150 | 0.2231 | 0.6363 | 0.7169 | 0.9676 | nan | 0.4492 | 0.9846 | nan | 0.3056 | 0.9671 |
0.2106 | 80.0 | 160 | 0.2089 | 0.6399 | 0.7152 | 0.9688 | nan | 0.4444 | 0.9860 | nan | 0.3114 | 0.9683 |
0.1995 | 85.0 | 170 | 0.1969 | 0.6493 | 0.7179 | 0.9709 | nan | 0.4478 | 0.9880 | nan | 0.3281 | 0.9704 |
0.1981 | 90.0 | 180 | 0.1909 | 0.6503 | 0.7136 | 0.9716 | nan | 0.4381 | 0.9892 | nan | 0.3293 | 0.9712 |
0.1875 | 95.0 | 190 | 0.1965 | 0.6473 | 0.7231 | 0.9697 | nan | 0.4598 | 0.9864 | nan | 0.3254 | 0.9692 |
0.2088 | 100.0 | 200 | 0.1952 | 0.6486 | 0.7199 | 0.9704 | nan | 0.4523 | 0.9874 | nan | 0.3271 | 0.9700 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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