--- license: other base_model: nvidia/mit-b1 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b1-finetuned-HikingHD results: [] --- # segformer-b1-finetuned-HikingHD This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the twdent/HikingHD dataset. It achieves the following results on the evaluation set: - Loss: 0.1067 - Mean Iou: 0.9379 - Mean Accuracy: 0.9665 - Overall Accuracy: 0.9684 - Accuracy Unlabeled: nan - Accuracy Traversable: 0.9485 - Accuracy Non-traversable: 0.9845 - Iou Unlabeled: nan - Iou Traversable: 0.9305 - Iou Non-traversable: 0.9452 - Local Tests: - Average inference time: 0.2622481801774767 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:| | 0.3796 | 1.67 | 20 | 0.5835 | 0.6174 | 0.9605 | 0.9617 | nan | 0.9488 | 0.9721 | 0.0 | 0.9180 | 0.9343 | | 0.3086 | 3.33 | 40 | 0.2597 | 0.9230 | 0.9589 | 0.9605 | nan | 0.9439 | 0.9739 | nan | 0.9143 | 0.9318 | | 0.2717 | 5.0 | 60 | 0.2202 | 0.9386 | 0.9681 | 0.9687 | nan | 0.9626 | 0.9736 | nan | 0.9321 | 0.9451 | | 0.2655 | 6.67 | 80 | 0.2127 | 0.9334 | 0.9658 | 0.9659 | nan | 0.9646 | 0.9670 | nan | 0.9267 | 0.9402 | | 0.1603 | 8.33 | 100 | 0.1699 | 0.9383 | 0.9677 | 0.9686 | nan | 0.9601 | 0.9753 | nan | 0.9316 | 0.9450 | | 0.2 | 10.0 | 120 | 0.1692 | 0.9289 | 0.9609 | 0.9637 | nan | 0.9342 | 0.9876 | nan | 0.9200 | 0.9378 | | 0.1613 | 11.67 | 140 | 0.1389 | 0.9399 | 0.9676 | 0.9695 | nan | 0.9498 | 0.9853 | nan | 0.9328 | 0.9470 | | 0.185 | 13.33 | 160 | 0.1612 | 0.9217 | 0.9566 | 0.9600 | nan | 0.9254 | 0.9878 | nan | 0.9116 | 0.9318 | | 0.251 | 15.0 | 180 | 0.1461 | 0.9277 | 0.9603 | 0.9631 | nan | 0.9340 | 0.9865 | nan | 0.9187 | 0.9368 | | 0.1038 | 16.67 | 200 | 0.1401 | 0.9248 | 0.9581 | 0.9616 | nan | 0.9258 | 0.9904 | nan | 0.9149 | 0.9346 | | 0.0628 | 18.33 | 220 | 0.1556 | 0.9195 | 0.9548 | 0.9588 | nan | 0.9171 | 0.9924 | nan | 0.9086 | 0.9303 | | 0.077 | 20.0 | 240 | 0.1439 | 0.9213 | 0.9561 | 0.9598 | nan | 0.9220 | 0.9902 | nan | 0.9110 | 0.9317 | | 0.0714 | 21.67 | 260 | 0.1267 | 0.9344 | 0.9641 | 0.9666 | nan | 0.9404 | 0.9878 | nan | 0.9263 | 0.9425 | | 0.081 | 23.33 | 280 | 0.1097 | 0.9397 | 0.9672 | 0.9694 | nan | 0.9470 | 0.9874 | nan | 0.9324 | 0.9470 | | 0.09 | 25.0 | 300 | 0.1063 | 0.9402 | 0.9679 | 0.9696 | nan | 0.9522 | 0.9836 | nan | 0.9332 | 0.9472 | | 0.0737 | 26.67 | 320 | 0.1045 | 0.9395 | 0.9674 | 0.9692 | nan | 0.9502 | 0.9845 | nan | 0.9323 | 0.9466 | | 0.1173 | 28.33 | 340 | 0.1019 | 0.9427 | 0.9702 | 0.9708 | nan | 0.9644 | 0.9760 | nan | 0.9365 | 0.9488 | | 0.0535 | 30.0 | 360 | 0.1132 | 0.9387 | 0.9674 | 0.9688 | nan | 0.9549 | 0.9799 | nan | 0.9317 | 0.9456 | | 0.0693 | 31.67 | 380 | 0.1182 | 0.9340 | 0.9637 | 0.9664 | nan | 0.9389 | 0.9886 | nan | 0.9258 | 0.9422 | | 0.0649 | 33.33 | 400 | 0.1108 | 0.9374 | 0.9662 | 0.9681 | nan | 0.9483 | 0.9841 | nan | 0.9300 | 0.9448 | | 0.1581 | 35.0 | 420 | 0.1107 | 0.9368 | 0.9658 | 0.9678 | nan | 0.9473 | 0.9844 | nan | 0.9293 | 0.9443 | | 0.0711 | 36.67 | 440 | 0.1011 | 0.9414 | 0.9690 | 0.9702 | nan | 0.9578 | 0.9801 | nan | 0.9348 | 0.9479 | | 0.0743 | 38.33 | 460 | 0.1026 | 0.9400 | 0.9676 | 0.9695 | nan | 0.9500 | 0.9853 | nan | 0.9329 | 0.9471 | | 0.0602 | 40.0 | 480 | 0.1029 | 0.9407 | 0.9681 | 0.9699 | nan | 0.9521 | 0.9841 | nan | 0.9337 | 0.9476 | | 0.0768 | 41.67 | 500 | 0.1059 | 0.9386 | 0.9670 | 0.9688 | nan | 0.9502 | 0.9837 | nan | 0.9314 | 0.9458 | | 0.0494 | 43.33 | 520 | 0.1076 | 0.9375 | 0.9663 | 0.9682 | nan | 0.9484 | 0.9842 | nan | 0.9302 | 0.9449 | | 0.0359 | 45.0 | 540 | 0.1097 | 0.9369 | 0.9659 | 0.9679 | nan | 0.9473 | 0.9844 | nan | 0.9294 | 0.9444 | | 0.0799 | 46.67 | 560 | 0.1070 | 0.9379 | 0.9666 | 0.9684 | nan | 0.9493 | 0.9838 | nan | 0.9306 | 0.9452 | | 0.0685 | 48.33 | 580 | 0.1075 | 0.9378 | 0.9665 | 0.9684 | nan | 0.9489 | 0.9841 | nan | 0.9305 | 0.9452 | | 0.0437 | 50.0 | 600 | 0.1067 | 0.9379 | 0.9665 | 0.9684 | nan | 0.9485 | 0.9845 | nan | 0.9305 | 0.9452 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0