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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: custom-object-test7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# custom-object-test7
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sungile/custom-object-masking5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1507
- Mean Iou: 0.3557
- Mean Accuracy: 0.7113
- Overall Accuracy: 0.7113
- Accuracy Unknown: nan
- Accuracy Background: 0.7113
- Accuracy Object: nan
- Iou Unknown: 0.0
- Iou Background: 0.7113
- Iou Object: nan
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 13
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unknown | Accuracy Background | Accuracy Object | Iou Unknown | Iou Background | Iou Object |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------------:|:---------------:|:-----------:|:--------------:|:----------:|
| 0.7249 | 0.25 | 20 | 0.9434 | 0.2871 | 0.8614 | 0.8614 | nan | 0.8614 | nan | 0.0 | 0.8614 | 0.0 |
| 0.7121 | 0.5 | 40 | 0.6610 | 0.3810 | 0.7620 | 0.7620 | nan | 0.7620 | nan | 0.0 | 0.7620 | nan |
| 0.4732 | 0.75 | 60 | 0.5195 | 0.3888 | 0.7775 | 0.7775 | nan | 0.7775 | nan | 0.0 | 0.7775 | nan |
| 0.5129 | 1.0 | 80 | 0.4134 | 0.2955 | 0.5910 | 0.5910 | nan | 0.5910 | nan | 0.0 | 0.5910 | nan |
| 0.4451 | 1.25 | 100 | 0.3473 | 0.2485 | 0.4971 | 0.4971 | nan | 0.4971 | nan | 0.0 | 0.4971 | nan |
| 0.4109 | 1.5 | 120 | 0.3279 | 0.2516 | 0.5033 | 0.5033 | nan | 0.5033 | nan | 0.0 | 0.5033 | nan |
| 0.3659 | 1.75 | 140 | 0.3014 | 0.2695 | 0.5391 | 0.5391 | nan | 0.5391 | nan | 0.0 | 0.5391 | nan |
| 0.2206 | 2.0 | 160 | 0.2773 | 0.2581 | 0.5163 | 0.5163 | nan | 0.5163 | nan | 0.0 | 0.5163 | nan |
| 0.2452 | 2.25 | 180 | 0.2575 | 0.2982 | 0.5964 | 0.5964 | nan | 0.5964 | nan | 0.0 | 0.5964 | nan |
| 0.2496 | 2.5 | 200 | 0.2523 | 0.3345 | 0.6690 | 0.6690 | nan | 0.6690 | nan | 0.0 | 0.6690 | nan |
| 0.1633 | 2.75 | 220 | 0.3160 | 0.4074 | 0.8149 | 0.8149 | nan | 0.8149 | nan | 0.0 | 0.8149 | nan |
| 0.1426 | 3.0 | 240 | 0.2242 | 0.3451 | 0.6903 | 0.6903 | nan | 0.6903 | nan | 0.0 | 0.6903 | nan |
| 0.1363 | 3.25 | 260 | 0.2225 | 0.3505 | 0.7010 | 0.7010 | nan | 0.7010 | nan | 0.0 | 0.7010 | nan |
| 0.1337 | 3.5 | 280 | 0.2229 | 0.3799 | 0.7599 | 0.7599 | nan | 0.7599 | nan | 0.0 | 0.7599 | nan |
| 0.1611 | 3.75 | 300 | 0.1971 | 0.3535 | 0.7070 | 0.7070 | nan | 0.7070 | nan | 0.0 | 0.7070 | nan |
| 0.1376 | 4.0 | 320 | 0.1835 | 0.3504 | 0.7007 | 0.7007 | nan | 0.7007 | nan | 0.0 | 0.7007 | nan |
| 0.1367 | 4.25 | 340 | 0.1735 | 0.3226 | 0.6453 | 0.6453 | nan | 0.6453 | nan | 0.0 | 0.6453 | nan |
| 0.1452 | 4.5 | 360 | 0.1689 | 0.3096 | 0.6192 | 0.6192 | nan | 0.6192 | nan | 0.0 | 0.6192 | nan |
| 0.1323 | 4.75 | 380 | 0.1741 | 0.3343 | 0.6685 | 0.6685 | nan | 0.6685 | nan | 0.0 | 0.6685 | nan |
| 0.1519 | 5.0 | 400 | 0.1647 | 0.3433 | 0.6866 | 0.6866 | nan | 0.6866 | nan | 0.0 | 0.6866 | nan |
| 0.1013 | 5.25 | 420 | 0.1645 | 0.3575 | 0.7149 | 0.7149 | nan | 0.7149 | nan | 0.0 | 0.7149 | nan |
| 0.0967 | 5.5 | 440 | 0.1645 | 0.3620 | 0.7241 | 0.7241 | nan | 0.7241 | nan | 0.0 | 0.7241 | nan |
| 0.1306 | 5.75 | 460 | 0.1646 | 0.3262 | 0.6523 | 0.6523 | nan | 0.6523 | nan | 0.0 | 0.6523 | nan |
| 0.2066 | 6.0 | 480 | 0.1600 | 0.3326 | 0.6651 | 0.6651 | nan | 0.6651 | nan | 0.0 | 0.6651 | nan |
| 0.0671 | 6.25 | 500 | 0.1546 | 0.3433 | 0.6867 | 0.6867 | nan | 0.6867 | nan | 0.0 | 0.6867 | nan |
| 0.0644 | 6.5 | 520 | 0.1612 | 0.3284 | 0.6568 | 0.6568 | nan | 0.6568 | nan | 0.0 | 0.6568 | nan |
| 0.0518 | 6.75 | 540 | 0.1575 | 0.3633 | 0.7266 | 0.7266 | nan | 0.7266 | nan | 0.0 | 0.7266 | nan |
| 0.086 | 7.0 | 560 | 0.1535 | 0.3490 | 0.6980 | 0.6980 | nan | 0.6980 | nan | 0.0 | 0.6980 | nan |
| 0.0602 | 7.25 | 580 | 0.1624 | 0.2988 | 0.5976 | 0.5976 | nan | 0.5976 | nan | 0.0 | 0.5976 | nan |
| 0.1332 | 7.5 | 600 | 0.1530 | 0.3532 | 0.7063 | 0.7063 | nan | 0.7063 | nan | 0.0 | 0.7063 | nan |
| 0.0494 | 7.75 | 620 | 0.1461 | 0.3732 | 0.7465 | 0.7465 | nan | 0.7465 | nan | 0.0 | 0.7465 | nan |
| 0.0685 | 8.0 | 640 | 0.1554 | 0.3198 | 0.6396 | 0.6396 | nan | 0.6396 | nan | 0.0 | 0.6396 | nan |
| 0.0492 | 8.25 | 660 | 0.1484 | 0.3563 | 0.7125 | 0.7125 | nan | 0.7125 | nan | 0.0 | 0.7125 | nan |
| 0.0525 | 8.5 | 680 | 0.1485 | 0.3341 | 0.6681 | 0.6681 | nan | 0.6681 | nan | 0.0 | 0.6681 | nan |
| 0.0911 | 8.75 | 700 | 0.1553 | 0.3257 | 0.6515 | 0.6515 | nan | 0.6515 | nan | 0.0 | 0.6515 | nan |
| 0.0493 | 9.0 | 720 | 0.1481 | 0.3598 | 0.7197 | 0.7197 | nan | 0.7197 | nan | 0.0 | 0.7197 | nan |
| 0.0445 | 9.25 | 740 | 0.1540 | 0.3536 | 0.7073 | 0.7073 | nan | 0.7073 | nan | 0.0 | 0.7073 | nan |
| 0.0723 | 9.5 | 760 | 0.1481 | 0.3461 | 0.6921 | 0.6921 | nan | 0.6921 | nan | 0.0 | 0.6921 | nan |
| 0.047 | 9.75 | 780 | 0.1479 | 0.3495 | 0.6990 | 0.6990 | nan | 0.6990 | nan | 0.0 | 0.6990 | nan |
| 0.0703 | 10.0 | 800 | 0.1489 | 0.3553 | 0.7106 | 0.7106 | nan | 0.7106 | nan | 0.0 | 0.7106 | nan |
| 0.0532 | 10.25 | 820 | 0.1475 | 0.3635 | 0.7270 | 0.7270 | nan | 0.7270 | nan | 0.0 | 0.7270 | nan |
| 0.0407 | 10.5 | 840 | 0.1481 | 0.3540 | 0.7079 | 0.7079 | nan | 0.7079 | nan | 0.0 | 0.7079 | nan |
| 0.0395 | 10.75 | 860 | 0.1545 | 0.3430 | 0.6861 | 0.6861 | nan | 0.6861 | nan | 0.0 | 0.6861 | nan |
| 0.1379 | 11.0 | 880 | 0.1531 | 0.3427 | 0.6854 | 0.6854 | nan | 0.6854 | nan | 0.0 | 0.6854 | nan |
| 0.0281 | 11.25 | 900 | 0.1483 | 0.3466 | 0.6932 | 0.6932 | nan | 0.6932 | nan | 0.0 | 0.6932 | nan |
| 0.0361 | 11.5 | 920 | 0.1560 | 0.3424 | 0.6848 | 0.6848 | nan | 0.6848 | nan | 0.0 | 0.6848 | nan |
| 0.0422 | 11.75 | 940 | 0.1494 | 0.3576 | 0.7152 | 0.7152 | nan | 0.7152 | nan | 0.0 | 0.7152 | nan |
| 0.0541 | 12.0 | 960 | 0.1502 | 0.3552 | 0.7105 | 0.7105 | nan | 0.7105 | nan | 0.0 | 0.7105 | nan |
| 0.0568 | 12.25 | 980 | 0.1549 | 0.3442 | 0.6885 | 0.6885 | nan | 0.6885 | nan | 0.0 | 0.6885 | nan |
| 0.0881 | 12.5 | 1000 | 0.1516 | 0.3481 | 0.6963 | 0.6963 | nan | 0.6963 | nan | 0.0 | 0.6963 | nan |
| 0.0478 | 12.75 | 1020 | 0.1527 | 0.3524 | 0.7047 | 0.7047 | nan | 0.7047 | nan | 0.0 | 0.7047 | nan |
| 0.0529 | 13.0 | 1040 | 0.1507 | 0.3557 | 0.7113 | 0.7113 | nan | 0.7113 | nan | 0.0 | 0.7113 | nan |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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