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SegFormer_b2_2

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segformer
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  1. README.md +125 -0
  2. config.json +112 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: other
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+ base_model: nvidia/segformer-b2-finetuned-cityscapes-1024-1024
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: SegFormer_b2_2
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+ results: []
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+ ---
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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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+
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+ # SegFormer_b2_2
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+
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+ This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b2-finetuned-cityscapes-1024-1024) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.9606
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+ - Mean Iou: 0.6207
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+ - Mean Accuracy: 0.7217
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+ - Overall Accuracy: 0.9249
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+ - Accuracy Road: 0.9844
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+ - Accuracy Sidewalk: 0.8451
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+ - Accuracy Building: 0.9446
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+ - Accuracy Wall: 0.5047
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+ - Accuracy Fence: 0.5505
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+ - Accuracy Pole: 0.5247
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+ - Accuracy Traffic light: 0.6721
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+ - Accuracy Traffic sign: 0.7141
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+ - Accuracy Vegetation: 0.9260
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+ - Accuracy Terrain: 0.6527
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+ - Accuracy Sky: 0.9398
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+ - Accuracy Person: 0.7681
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+ - Accuracy Rider: 0.5605
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+ - Accuracy Car: 0.9182
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+ - Accuracy Truck: 0.5968
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+ - Accuracy Bus: 0.7881
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+ - Accuracy Train: 0.5123
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+ - Accuracy Motorcycle: 0.5706
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+ - Accuracy Bicycle: 0.7386
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+ - Iou Road: 0.9579
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+ - Iou Sidewalk: 0.7271
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+ - Iou Building: 0.8669
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+ - Iou Wall: 0.4481
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+ - Iou Fence: 0.4465
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+ - Iou Pole: 0.3530
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+ - Iou Traffic light: 0.4760
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+ - Iou Traffic sign: 0.5823
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+ - Iou Vegetation: 0.8765
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+ - Iou Terrain: 0.5576
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+ - Iou Sky: 0.9035
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+ - Iou Person: 0.5932
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+ - Iou Rider: 0.3732
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+ - Iou Car: 0.8710
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+ - Iou Truck: 0.5520
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+ - Iou Bus: 0.7234
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+ - Iou Train: 0.4947
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+ - Iou Motorcycle: 0.4111
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+ - Iou Bicycle: 0.5787
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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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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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 50
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Road | Accuracy Sidewalk | Accuracy Building | Accuracy Wall | Accuracy Fence | Accuracy Pole | Accuracy Traffic light | Accuracy Traffic sign | Accuracy Vegetation | Accuracy Terrain | Accuracy Sky | Accuracy Person | Accuracy Rider | Accuracy Car | Accuracy Truck | Accuracy Bus | Accuracy Train | Accuracy Motorcycle | Accuracy Bicycle | Iou Road | Iou Sidewalk | Iou Building | Iou Wall | Iou Fence | Iou Pole | Iou Traffic light | Iou Traffic sign | Iou Vegetation | Iou Terrain | Iou Sky | Iou Person | Iou Rider | Iou Car | Iou Truck | Iou Bus | Iou Train | Iou Motorcycle | Iou Bicycle |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:-----------------:|:-----------------:|:-------------:|:--------------:|:-------------:|:----------------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:---------------:|:--------------:|:------------:|:--------------:|:------------:|:--------------:|:-------------------:|:----------------:|:--------:|:------------:|:------------:|:--------:|:---------:|:--------:|:-----------------:|:----------------:|:--------------:|:-----------:|:-------:|:----------:|:---------:|:-------:|:---------:|:-------:|:---------:|:--------------:|:-----------:|
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+ | 16.6751 | 2.1290 | 100 | 2.9556 | 0.5273 | 0.5860 | 0.9128 | 0.9832 | 0.7636 | 0.9520 | 0.3754 | 0.3619 | 0.3673 | 0.3866 | 0.5925 | 0.9283 | 0.6073 | 0.8979 | 0.7549 | 0.2971 | 0.9528 | 0.3839 | 0.5714 | 0.1148 | 0.1847 | 0.6577 | 0.9480 | 0.6592 | 0.8484 | 0.3642 | 0.3387 | 0.2441 | 0.3544 | 0.5052 | 0.8692 | 0.5569 | 0.8726 | 0.5470 | 0.2646 | 0.8455 | 0.3804 | 0.5634 | 0.1148 | 0.1829 | 0.5595 |
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+ | 14.9709 | 4.2581 | 200 | 2.3312 | 0.5652 | 0.6527 | 0.9067 | 0.9790 | 0.6836 | 0.9433 | 0.4577 | 0.4285 | 0.4349 | 0.5329 | 0.6837 | 0.9148 | 0.5706 | 0.9374 | 0.7045 | 0.5310 | 0.9232 | 0.4789 | 0.6725 | 0.3114 | 0.4178 | 0.7961 | 0.9282 | 0.5672 | 0.8545 | 0.4214 | 0.3899 | 0.2939 | 0.4030 | 0.5330 | 0.8612 | 0.5129 | 0.8959 | 0.5318 | 0.3426 | 0.8511 | 0.4733 | 0.6519 | 0.3102 | 0.3764 | 0.5404 |
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+ | 12.5514 | 6.3871 | 300 | 2.2463 | 0.5818 | 0.6824 | 0.9099 | 0.9828 | 0.7372 | 0.9385 | 0.4104 | 0.5640 | 0.4339 | 0.6397 | 0.6520 | 0.9092 | 0.6454 | 0.9135 | 0.7698 | 0.5094 | 0.9151 | 0.5407 | 0.7326 | 0.5067 | 0.4544 | 0.7098 | 0.9406 | 0.6260 | 0.8504 | 0.3943 | 0.4213 | 0.2925 | 0.3770 | 0.5370 | 0.8563 | 0.5244 | 0.8847 | 0.5331 | 0.3304 | 0.8544 | 0.5204 | 0.7059 | 0.4756 | 0.3823 | 0.5481 |
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+ | 11.8215 | 8.5161 | 400 | 2.1854 | 0.5677 | 0.6688 | 0.9123 | 0.9798 | 0.7777 | 0.9276 | 0.5374 | 0.5522 | 0.4754 | 0.6585 | 0.6626 | 0.9189 | 0.6628 | 0.9496 | 0.7286 | 0.5013 | 0.9211 | 0.4601 | 0.7198 | 0.2690 | 0.3037 | 0.7014 | 0.9419 | 0.6442 | 0.8586 | 0.4761 | 0.4304 | 0.3157 | 0.3894 | 0.5386 | 0.8601 | 0.4801 | 0.8998 | 0.5440 | 0.3374 | 0.8497 | 0.4551 | 0.6616 | 0.2649 | 0.2853 | 0.5536 |
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+ | 12.7976 | 10.6452 | 500 | 2.1913 | 0.5681 | 0.6577 | 0.9132 | 0.9788 | 0.8357 | 0.9403 | 0.3648 | 0.4464 | 0.5153 | 0.5844 | 0.6711 | 0.9064 | 0.5650 | 0.9370 | 0.7434 | 0.5706 | 0.9340 | 0.4380 | 0.5866 | 0.5737 | 0.2666 | 0.6390 | 0.9501 | 0.6739 | 0.8517 | 0.3396 | 0.3917 | 0.3219 | 0.4339 | 0.5206 | 0.8594 | 0.5195 | 0.8937 | 0.5428 | 0.3339 | 0.8485 | 0.4335 | 0.5700 | 0.5384 | 0.2364 | 0.5346 |
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+ | 13.2234 | 12.7742 | 600 | 2.1208 | 0.5839 | 0.6746 | 0.9159 | 0.9840 | 0.8034 | 0.9334 | 0.4014 | 0.5319 | 0.4315 | 0.5946 | 0.6512 | 0.9280 | 0.6488 | 0.9267 | 0.6349 | 0.5981 | 0.9371 | 0.4992 | 0.7449 | 0.4463 | 0.3991 | 0.7225 | 0.9498 | 0.6801 | 0.8532 | 0.3739 | 0.4087 | 0.3091 | 0.4380 | 0.5276 | 0.8633 | 0.5504 | 0.8880 | 0.5185 | 0.3530 | 0.8580 | 0.4889 | 0.7079 | 0.4403 | 0.3343 | 0.5509 |
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+ | 9.7371 | 14.9032 | 700 | 2.1398 | 0.5802 | 0.6915 | 0.9116 | 0.9799 | 0.8241 | 0.9162 | 0.5241 | 0.5775 | 0.5446 | 0.6767 | 0.6958 | 0.9263 | 0.5810 | 0.9162 | 0.6330 | 0.5151 | 0.8971 | 0.5783 | 0.8181 | 0.3773 | 0.4174 | 0.7405 | 0.9472 | 0.6630 | 0.8494 | 0.4527 | 0.4216 | 0.3189 | 0.4272 | 0.5349 | 0.8626 | 0.4880 | 0.8874 | 0.5101 | 0.3422 | 0.8515 | 0.5549 | 0.6742 | 0.3667 | 0.3440 | 0.5263 |
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+ | 8.4735 | 17.0215 | 800 | 2.1760 | 0.5756 | 0.6897 | 0.9129 | 0.9871 | 0.7981 | 0.9380 | 0.5897 | 0.6268 | 0.4616 | 0.6864 | 0.6777 | 0.8896 | 0.6343 | 0.9445 | 0.6620 | 0.6210 | 0.8976 | 0.5353 | 0.7562 | 0.3340 | 0.2657 | 0.7991 | 0.9503 | 0.6854 | 0.8490 | 0.4706 | 0.3476 | 0.3187 | 0.4378 | 0.5301 | 0.8558 | 0.5319 | 0.8957 | 0.5317 | 0.3490 | 0.8504 | 0.5175 | 0.6891 | 0.3327 | 0.2495 | 0.5435 |
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+ | 7.8868 | 19.1505 | 900 | 2.0991 | 0.6117 | 0.7249 | 0.9189 | 0.9799 | 0.8504 | 0.9266 | 0.5342 | 0.6147 | 0.5391 | 0.6287 | 0.6909 | 0.9071 | 0.7484 | 0.9448 | 0.7911 | 0.5599 | 0.9310 | 0.6343 | 0.7929 | 0.5248 | 0.4726 | 0.7015 | 0.9576 | 0.7113 | 0.8583 | 0.4561 | 0.4635 | 0.3363 | 0.4626 | 0.5534 | 0.8615 | 0.4779 | 0.8956 | 0.5629 | 0.3528 | 0.8729 | 0.5908 | 0.7386 | 0.5084 | 0.4023 | 0.5594 |
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+ | 9.4082 | 21.2796 | 1000 | 2.0767 | 0.5897 | 0.6879 | 0.9169 | 0.9831 | 0.8115 | 0.9418 | 0.4610 | 0.5540 | 0.5098 | 0.6356 | 0.6613 | 0.9094 | 0.6206 | 0.9356 | 0.7755 | 0.6345 | 0.9301 | 0.5099 | 0.7109 | 0.4752 | 0.4408 | 0.5701 | 0.9521 | 0.6884 | 0.8547 | 0.4137 | 0.4308 | 0.3356 | 0.4546 | 0.5351 | 0.8659 | 0.5416 | 0.8965 | 0.5601 | 0.3526 | 0.8540 | 0.4773 | 0.6614 | 0.4609 | 0.3696 | 0.4994 |
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+ | 6.9498 | 23.4086 | 1100 | 2.1655 | 0.5859 | 0.6856 | 0.9165 | 0.9772 | 0.8440 | 0.9373 | 0.4009 | 0.6115 | 0.4860 | 0.5818 | 0.6818 | 0.9242 | 0.5435 | 0.9505 | 0.7132 | 0.5791 | 0.9152 | 0.4764 | 0.7693 | 0.4355 | 0.5341 | 0.6645 | 0.9516 | 0.6906 | 0.8536 | 0.3534 | 0.4365 | 0.3254 | 0.4377 | 0.5631 | 0.8669 | 0.4874 | 0.8886 | 0.5631 | 0.3553 | 0.8587 | 0.4529 | 0.6921 | 0.4264 | 0.3786 | 0.5496 |
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+ | 7.1755 | 25.5376 | 1200 | 2.0846 | 0.5910 | 0.6962 | 0.9183 | 0.9765 | 0.8536 | 0.9404 | 0.5186 | 0.5747 | 0.5408 | 0.6658 | 0.6923 | 0.9122 | 0.5719 | 0.9314 | 0.7621 | 0.5426 | 0.9261 | 0.5853 | 0.7995 | 0.2802 | 0.4453 | 0.7091 | 0.9536 | 0.6891 | 0.8609 | 0.4342 | 0.4450 | 0.3391 | 0.4558 | 0.5683 | 0.8679 | 0.5108 | 0.8998 | 0.5627 | 0.3550 | 0.8639 | 0.5502 | 0.6730 | 0.2792 | 0.3643 | 0.5565 |
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+ | 6.4426 | 27.6667 | 1300 | 2.0718 | 0.6111 | 0.7120 | 0.9207 | 0.9847 | 0.8011 | 0.9443 | 0.4555 | 0.5963 | 0.5093 | 0.6059 | 0.7045 | 0.9149 | 0.6177 | 0.9418 | 0.7521 | 0.5612 | 0.9359 | 0.5108 | 0.7845 | 0.6642 | 0.5255 | 0.7186 | 0.9539 | 0.6946 | 0.8617 | 0.4206 | 0.4598 | 0.3410 | 0.4472 | 0.5656 | 0.8704 | 0.5400 | 0.9032 | 0.5576 | 0.3591 | 0.8706 | 0.4810 | 0.7120 | 0.6166 | 0.4026 | 0.5532 |
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+ | 7.9571 | 29.7957 | 1400 | 2.1007 | 0.6176 | 0.7312 | 0.9220 | 0.9852 | 0.8278 | 0.9265 | 0.4403 | 0.6432 | 0.5272 | 0.6923 | 0.7098 | 0.9338 | 0.6698 | 0.9471 | 0.7186 | 0.6121 | 0.9187 | 0.5939 | 0.7617 | 0.7162 | 0.5277 | 0.7412 | 0.9586 | 0.7228 | 0.8621 | 0.4078 | 0.4189 | 0.3411 | 0.4602 | 0.5732 | 0.8725 | 0.5544 | 0.9018 | 0.5617 | 0.3598 | 0.8695 | 0.5311 | 0.6980 | 0.6668 | 0.4134 | 0.5598 |
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+ | 7.0214 | 31.9247 | 1500 | 2.0496 | 0.6164 | 0.7258 | 0.9226 | 0.9819 | 0.8393 | 0.9301 | 0.5485 | 0.5417 | 0.5299 | 0.6742 | 0.7202 | 0.9290 | 0.7253 | 0.9414 | 0.7524 | 0.5288 | 0.9318 | 0.5999 | 0.8061 | 0.5190 | 0.5778 | 0.7141 | 0.9578 | 0.7141 | 0.8628 | 0.4576 | 0.4463 | 0.3425 | 0.4592 | 0.5755 | 0.8727 | 0.5648 | 0.8974 | 0.5800 | 0.3549 | 0.8754 | 0.5577 | 0.7254 | 0.5025 | 0.4217 | 0.5440 |
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+ | 7.6246 | 34.0430 | 1600 | 2.0286 | 0.6136 | 0.7167 | 0.9223 | 0.9861 | 0.8199 | 0.9443 | 0.5169 | 0.5326 | 0.5077 | 0.6671 | 0.7165 | 0.9199 | 0.6715 | 0.9394 | 0.6962 | 0.5626 | 0.9206 | 0.6453 | 0.7967 | 0.4386 | 0.5843 | 0.7517 | 0.9552 | 0.7119 | 0.8610 | 0.4590 | 0.4396 | 0.3443 | 0.4611 | 0.5784 | 0.8721 | 0.5778 | 0.8948 | 0.5691 | 0.3693 | 0.8707 | 0.5520 | 0.7157 | 0.4265 | 0.4399 | 0.5592 |
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+ | 8.3015 | 36.1720 | 1700 | 2.0353 | 0.6179 | 0.7146 | 0.9237 | 0.9838 | 0.8350 | 0.9425 | 0.5130 | 0.5482 | 0.5338 | 0.6419 | 0.7140 | 0.9251 | 0.6892 | 0.9382 | 0.7216 | 0.5111 | 0.9258 | 0.6673 | 0.7389 | 0.6655 | 0.3349 | 0.7471 | 0.9571 | 0.7172 | 0.8667 | 0.4434 | 0.4582 | 0.3534 | 0.4672 | 0.5753 | 0.8749 | 0.5397 | 0.9001 | 0.5732 | 0.3670 | 0.8700 | 0.5908 | 0.6889 | 0.6197 | 0.3053 | 0.5727 |
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+ | 6.6871 | 38.3011 | 1800 | 2.0154 | 0.6197 | 0.7235 | 0.9232 | 0.9853 | 0.8336 | 0.9387 | 0.5135 | 0.5580 | 0.5278 | 0.6452 | 0.7185 | 0.9232 | 0.6777 | 0.9465 | 0.7381 | 0.5634 | 0.9209 | 0.6325 | 0.7569 | 0.6470 | 0.5012 | 0.7192 | 0.9559 | 0.7157 | 0.8662 | 0.4477 | 0.4471 | 0.3452 | 0.4562 | 0.5791 | 0.8745 | 0.5668 | 0.9031 | 0.5781 | 0.3611 | 0.8668 | 0.5610 | 0.6953 | 0.6048 | 0.3805 | 0.5695 |
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+ | 6.08 | 40.4301 | 1900 | 1.9764 | 0.6243 | 0.7265 | 0.9251 | 0.9837 | 0.8531 | 0.9402 | 0.5178 | 0.5974 | 0.5184 | 0.6617 | 0.6958 | 0.9265 | 0.6695 | 0.9388 | 0.7550 | 0.5007 | 0.9286 | 0.6250 | 0.7855 | 0.6083 | 0.5623 | 0.7346 | 0.9592 | 0.7311 | 0.8671 | 0.4530 | 0.4658 | 0.3482 | 0.4672 | 0.5793 | 0.8760 | 0.5586 | 0.8995 | 0.5776 | 0.3573 | 0.8744 | 0.5566 | 0.7267 | 0.5774 | 0.4128 | 0.5741 |
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+ | 6.2931 | 42.5591 | 2000 | 1.9826 | 0.6251 | 0.7327 | 0.9242 | 0.9827 | 0.8623 | 0.9415 | 0.5115 | 0.5476 | 0.5138 | 0.6720 | 0.7138 | 0.9219 | 0.6876 | 0.9317 | 0.7541 | 0.5426 | 0.9236 | 0.5970 | 0.7784 | 0.6505 | 0.6230 | 0.7656 | 0.9588 | 0.7279 | 0.8642 | 0.4530 | 0.4526 | 0.3484 | 0.4763 | 0.5815 | 0.8742 | 0.5669 | 0.8976 | 0.5785 | 0.3623 | 0.8725 | 0.5541 | 0.7159 | 0.6131 | 0.4113 | 0.5689 |
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+ | 7.358 | 44.6882 | 2100 | 1.9920 | 0.6200 | 0.7234 | 0.9252 | 0.9825 | 0.8570 | 0.9464 | 0.5359 | 0.5386 | 0.5277 | 0.6540 | 0.7065 | 0.9213 | 0.6690 | 0.9441 | 0.7394 | 0.5612 | 0.9279 | 0.6127 | 0.7689 | 0.5046 | 0.5876 | 0.7602 | 0.9600 | 0.7354 | 0.8655 | 0.4612 | 0.4359 | 0.3527 | 0.4766 | 0.5838 | 0.8746 | 0.5658 | 0.9032 | 0.5937 | 0.3755 | 0.8728 | 0.5446 | 0.7052 | 0.4878 | 0.4108 | 0.5756 |
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+ | 6.1954 | 46.8172 | 2200 | 1.9624 | 0.6220 | 0.7258 | 0.9248 | 0.9841 | 0.8487 | 0.9439 | 0.5136 | 0.5489 | 0.5316 | 0.6693 | 0.7274 | 0.9223 | 0.6580 | 0.9433 | 0.7663 | 0.5655 | 0.9195 | 0.6042 | 0.7952 | 0.5396 | 0.5698 | 0.7396 | 0.9582 | 0.7289 | 0.8669 | 0.4539 | 0.4452 | 0.3528 | 0.4768 | 0.5826 | 0.8755 | 0.5567 | 0.9042 | 0.5920 | 0.3709 | 0.8711 | 0.5540 | 0.7234 | 0.5178 | 0.4115 | 0.5767 |
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+ | 6.1774 | 48.9462 | 2300 | 1.9606 | 0.6207 | 0.7217 | 0.9249 | 0.9844 | 0.8451 | 0.9446 | 0.5047 | 0.5505 | 0.5247 | 0.6721 | 0.7141 | 0.9260 | 0.6527 | 0.9398 | 0.7681 | 0.5605 | 0.9182 | 0.5968 | 0.7881 | 0.5123 | 0.5706 | 0.7386 | 0.9579 | 0.7271 | 0.8669 | 0.4481 | 0.4465 | 0.3530 | 0.4760 | 0.5823 | 0.8765 | 0.5576 | 0.9035 | 0.5932 | 0.3732 | 0.8710 | 0.5520 | 0.7234 | 0.4947 | 0.4111 | 0.5787 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.1
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+ - Pytorch 2.1.2+cu121
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
config.json ADDED
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+ {
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+ "_name_or_path": "nvidia/segformer-b2-finetuned-cityscapes-1024-1024",
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+ "architectures": [
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+ "SegformerForSemanticSegmentation"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "classifier_dropout_prob": 0.1,
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+ "decoder_hidden_size": 768,
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+ "depths": [
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+ 3,
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+ 4,
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+ 6,
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+ 3
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+ ],
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+ "downsampling_rates": [
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+ 1,
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+ 4,
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+ 8,
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+ 16
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+ ],
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+ "drop_path_rate": 0.1,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_sizes": [
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+ 64,
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+ 128,
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+ 320,
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+ 512
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+ ],
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+ "id2label": {
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+ "0": "road",
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+ "1": "sidewalk",
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+ "2": "building",
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+ "3": "wall",
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+ "4": "fence",
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+ "5": "pole",
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+ "6": "traffic light",
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+ "7": "traffic sign",
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+ "8": "vegetation",
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+ "9": "terrain",
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+ "10": "sky",
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+ "11": "person",
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+ "12": "rider",
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+ "13": "car",
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+ "14": "truck",
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+ "15": "bus",
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+ "16": "train",
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+ "17": "motorcycle",
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+ "18": "bicycle"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "bicycle": 18,
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+ "building": 2,
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+ "bus": 15,
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+ "car": 13,
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