--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: twdent/segformer-b0-finetuned-robot-hiking results: [] --- # segformer-b0-finetuned-robot-hiking This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the twdent/Hiking dataset. It achieves the following results on the evaluation set: - Loss: 0.1458 - Mean Iou: 0.6158 - Mean Accuracy: 0.9603 - Overall Accuracy: 0.9619 - Accuracy Unlabeled: nan - Accuracy Traversable: 0.9546 - Accuracy Non-traversable: 0.9661 - Iou Unlabeled: 0.0 - Iou Traversable: 0.9044 - Iou Non-traversable: 0.9429 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:| | 0.5363 | 1.33 | 20 | 0.7433 | 0.5581 | 0.9246 | 0.9153 | nan | 0.9573 | 0.8919 | 0.0 | 0.8030 | 0.8712 | | 0.3689 | 2.67 | 40 | 0.4666 | 0.5567 | 0.9281 | 0.9137 | nan | 0.9791 | 0.8771 | 0.0 | 0.8030 | 0.8670 | | 0.2706 | 4.0 | 60 | 0.3204 | 0.5880 | 0.9479 | 0.9407 | nan | 0.9732 | 0.9226 | 0.0 | 0.8550 | 0.9089 | | 0.2338 | 5.33 | 80 | 0.2881 | 0.5985 | 0.9504 | 0.9497 | nan | 0.9527 | 0.9481 | 0.0 | 0.8719 | 0.9236 | | 0.2068 | 6.67 | 100 | 0.2556 | 0.9022 | 0.9521 | 0.9521 | nan | 0.9522 | 0.9521 | nan | 0.8770 | 0.9273 | | 0.1764 | 8.0 | 120 | 0.2401 | 0.6024 | 0.9539 | 0.9528 | nan | 0.9577 | 0.9500 | 0.0 | 0.8792 | 0.9280 | | 0.2639 | 9.33 | 140 | 0.2588 | 0.5937 | 0.9504 | 0.9455 | nan | 0.9680 | 0.9329 | 0.0 | 0.8646 | 0.9166 | | 0.1813 | 10.67 | 160 | 0.2124 | 0.6030 | 0.9526 | 0.9530 | nan | 0.9513 | 0.9540 | 0.0 | 0.8801 | 0.9287 | | 0.1407 | 12.0 | 180 | 0.1938 | 0.6055 | 0.9518 | 0.9554 | nan | 0.9388 | 0.9647 | 0.0 | 0.8836 | 0.9328 | | 0.13 | 13.33 | 200 | 0.1881 | 0.6062 | 0.9524 | 0.9558 | nan | 0.9403 | 0.9644 | 0.0 | 0.8854 | 0.9333 | | 0.107 | 14.67 | 220 | 0.2092 | 0.5967 | 0.9530 | 0.9474 | nan | 0.9725 | 0.9334 | 0.0 | 0.8708 | 0.9194 | | 0.1282 | 16.0 | 240 | 0.1803 | 0.6065 | 0.9536 | 0.9555 | nan | 0.9471 | 0.9602 | 0.0 | 0.8869 | 0.9328 | | 0.146 | 17.33 | 260 | 0.1912 | 0.6028 | 0.9559 | 0.9519 | nan | 0.9700 | 0.9418 | 0.0 | 0.8814 | 0.9269 | | 0.1011 | 18.67 | 280 | 0.1769 | 0.6079 | 0.9598 | 0.9561 | nan | 0.9727 | 0.9468 | 0.0 | 0.8907 | 0.9330 | | 0.1124 | 20.0 | 300 | 0.1580 | 0.6135 | 0.9582 | 0.9608 | nan | 0.9491 | 0.9673 | 0.0 | 0.8995 | 0.9411 | | 0.0801 | 21.33 | 320 | 0.1614 | 0.6113 | 0.9582 | 0.9588 | nan | 0.9563 | 0.9602 | 0.0 | 0.8960 | 0.9380 | | 0.0831 | 22.67 | 340 | 0.1540 | 0.6130 | 0.9608 | 0.9601 | nan | 0.9633 | 0.9584 | 0.0 | 0.8994 | 0.9396 | | 0.0599 | 24.0 | 360 | 0.1641 | 0.6098 | 0.9584 | 0.9576 | nan | 0.9614 | 0.9554 | 0.0 | 0.8935 | 0.9358 | | 0.0955 | 25.33 | 380 | 0.1711 | 0.6084 | 0.9597 | 0.9562 | nan | 0.9720 | 0.9474 | 0.0 | 0.8917 | 0.9334 | | 0.0667 | 26.67 | 400 | 0.1618 | 0.6109 | 0.9574 | 0.9583 | nan | 0.9543 | 0.9605 | 0.0 | 0.8954 | 0.9373 | | 0.0783 | 28.0 | 420 | 0.1640 | 0.6089 | 0.9589 | 0.9568 | nan | 0.9665 | 0.9513 | 0.0 | 0.8924 | 0.9343 | | 0.0743 | 29.33 | 440 | 0.1512 | 0.6145 | 0.9582 | 0.9612 | nan | 0.9478 | 0.9686 | 0.0 | 0.9016 | 0.9419 | | 0.0775 | 30.67 | 460 | 0.1574 | 0.6131 | 0.9583 | 0.9598 | nan | 0.9528 | 0.9637 | 0.0 | 0.8995 | 0.9398 | | 0.0773 | 32.0 | 480 | 0.1464 | 0.6157 | 0.9610 | 0.9621 | nan | 0.9573 | 0.9647 | 0.0 | 0.9043 | 0.9428 | | 0.0575 | 33.33 | 500 | 0.1600 | 0.6085 | 0.9568 | 0.9564 | nan | 0.9583 | 0.9554 | 0.0 | 0.8912 | 0.9343 | | 0.0729 | 34.67 | 520 | 0.1540 | 0.6105 | 0.9569 | 0.9577 | nan | 0.9541 | 0.9597 | 0.0 | 0.8946 | 0.9369 | | 0.1409 | 36.0 | 540 | 0.1557 | 0.6112 | 0.9584 | 0.9586 | nan | 0.9575 | 0.9593 | 0.0 | 0.8962 | 0.9376 | | 0.0543 | 37.33 | 560 | 0.1607 | 0.6103 | 0.9546 | 0.9581 | nan | 0.9422 | 0.9670 | 0.0 | 0.8938 | 0.9373 | | 0.063 | 38.67 | 580 | 0.1622 | 0.6099 | 0.9558 | 0.9574 | nan | 0.9504 | 0.9613 | 0.0 | 0.8933 | 0.9364 | | 0.0549 | 40.0 | 600 | 0.1543 | 0.6118 | 0.9571 | 0.9590 | nan | 0.9503 | 0.9639 | 0.0 | 0.8969 | 0.9386 | | 0.0766 | 41.33 | 620 | 0.1481 | 0.6139 | 0.9575 | 0.9606 | nan | 0.9467 | 0.9684 | 0.0 | 0.9005 | 0.9412 | | 0.0616 | 42.67 | 640 | 0.1485 | 0.6151 | 0.9600 | 0.9614 | nan | 0.9550 | 0.9649 | 0.0 | 0.9032 | 0.9422 | | 0.0872 | 44.0 | 660 | 0.1511 | 0.6144 | 0.9607 | 0.9609 | nan | 0.9602 | 0.9612 | 0.0 | 0.9022 | 0.9409 | | 0.0677 | 45.33 | 680 | 0.1510 | 0.6139 | 0.9599 | 0.9604 | nan | 0.9580 | 0.9618 | 0.0 | 0.9012 | 0.9405 | | 0.1075 | 46.67 | 700 | 0.1506 | 0.6145 | 0.9606 | 0.9610 | nan | 0.9592 | 0.9619 | 0.0 | 0.9024 | 0.9411 | | 0.0485 | 48.0 | 720 | 0.1450 | 0.6159 | 0.9595 | 0.9621 | nan | 0.9506 | 0.9685 | 0.0 | 0.9043 | 0.9433 | | 0.0972 | 49.33 | 740 | 0.1458 | 0.6158 | 0.9603 | 0.9619 | nan | 0.9546 | 0.9661 | 0.0 | 0.9044 | 0.9429 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0