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segformer-b0-finetuned-segments-greenhouse-jun-24

Image Segmentation
Transformers
PyTorch
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +117 -0
  2. config.json +102 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: other
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+ base_model: nvidia/mit-b0
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b0-finetuned-segments-greenhouse-jun-24
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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-b0-finetuned-segments-greenhouse-jun-24
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6502
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+ - Mean Iou: 0.3640
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+ - Mean Accuracy: 0.4319
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+ - Overall Accuracy: 0.8283
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Object: 0.0
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+ - Accuracy Road: 0.9324
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+ - Accuracy Plant: 0.8871
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+ - Accuracy Iron: 0.0017
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+ - Accuracy Wood: nan
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+ - Accuracy Wall: 0.7226
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+ - Accuracy Raw Road: 0.9465
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+ - Accuracy Bottom Wall: 0.0
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+ - Accuracy Roof: 0.0
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+ - Accuracy Grass: nan
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+ - Accuracy Mulch: 0.8289
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+ - Accuracy Person: nan
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+ - Accuracy Tomato: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Object: 0.0
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+ - Iou Road: 0.7525
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+ - Iou Plant: 0.7027
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+ - Iou Iron: 0.0017
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+ - Iou Wood: nan
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+ - Iou Wall: 0.5584
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+ - Iou Raw Road: 0.8998
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+ - Iou Bottom Wall: 0.0
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+ - Iou Roof: 0.0
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+ - Iou Grass: nan
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+ - Iou Mulch: 0.7252
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+ - Iou Person: nan
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+ - Iou Tomato: 0.0
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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: 6e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 30
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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 Unlabeled | Accuracy Object | Accuracy Road | Accuracy Plant | Accuracy Iron | Accuracy Wood | Accuracy Wall | Accuracy Raw Road | Accuracy Bottom Wall | Accuracy Roof | Accuracy Grass | Accuracy Mulch | Accuracy Person | Accuracy Tomato | Iou Unlabeled | Iou Object | Iou Road | Iou Plant | Iou Iron | Iou Wood | Iou Wall | Iou Raw Road | Iou Bottom Wall | Iou Roof | Iou Grass | Iou Mulch | Iou Person | Iou Tomato |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------:|:--------------:|:-------------:|:-------------:|:-------------:|:-----------------:|:--------------------:|:-------------:|:--------------:|:--------------:|:---------------:|:---------------:|:-------------:|:----------:|:--------:|:---------:|:--------:|:--------:|:--------:|:------------:|:---------------:|:--------:|:---------:|:---------:|:----------:|:----------:|
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+ | 1.9416 | 1.05 | 20 | 2.3650 | 0.1880 | 0.3464 | 0.6650 | nan | 0.0 | 0.7192 | 0.7931 | 0.2656 | nan | 0.0681 | 0.8201 | 0.0 | 0.0 | nan | 0.7950 | nan | 0.0029 | nan | 0.0 | 0.4874 | 0.5054 | 0.1242 | 0.0 | 0.0676 | 0.8065 | 0.0 | 0.0 | 0.0 | 0.4498 | 0.0 | 0.0027 |
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+ | 1.4047 | 2.11 | 40 | 1.6208 | 0.2889 | 0.3699 | 0.7203 | nan | 0.0 | 0.7452 | 0.8135 | 0.0384 | nan | 0.4353 | 0.8655 | 0.0 | 0.0 | nan | 0.8014 | nan | 0.0 | nan | 0.0 | 0.4970 | 0.5407 | 0.0371 | nan | 0.4041 | 0.8614 | 0.0 | 0.0 | nan | 0.5489 | nan | 0.0 |
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+ | 1.4998 | 3.16 | 60 | 1.2645 | 0.3150 | 0.3936 | 0.7522 | nan | 0.0 | 0.7532 | 0.8121 | 0.0174 | nan | 0.6304 | 0.9056 | 0.0 | 0.0 | nan | 0.8171 | nan | 0.0 | nan | 0.0 | 0.5316 | 0.5644 | 0.0174 | nan | 0.5346 | 0.8961 | 0.0 | 0.0 | nan | 0.6057 | nan | 0.0 |
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+ | 1.0844 | 4.21 | 80 | 1.1551 | 0.3234 | 0.4083 | 0.7685 | nan | 0.0 | 0.8290 | 0.7952 | 0.0230 | nan | 0.6585 | 0.9033 | 0.0 | 0.0 | nan | 0.8740 | nan | 0.0 | nan | 0.0 | 0.5971 | 0.5910 | 0.0229 | nan | 0.5307 | 0.8905 | 0.0 | 0.0 | nan | 0.6020 | nan | 0.0 |
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+ | 1.2949 | 5.26 | 100 | 1.0333 | 0.3363 | 0.4129 | 0.7841 | nan | 0.0 | 0.8274 | 0.8389 | 0.0140 | nan | 0.7114 | 0.9133 | 0.0 | 0.0 | nan | 0.8243 | nan | 0.0 | nan | 0.0 | 0.6211 | 0.6125 | 0.0140 | nan | 0.5854 | 0.8890 | 0.0 | 0.0 | nan | 0.6410 | nan | 0.0 |
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+ | 1.3389 | 6.32 | 120 | 0.9260 | 0.3417 | 0.4155 | 0.7932 | nan | 0.0 | 0.8668 | 0.8408 | 0.0 | nan | 0.7105 | 0.9202 | 0.0 | 0.0 | nan | 0.8164 | nan | 0.0 | nan | 0.0 | 0.6489 | 0.6214 | 0.0 | nan | 0.6039 | 0.8936 | 0.0 | 0.0 | nan | 0.6495 | nan | 0.0 |
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+ | 0.7833 | 7.37 | 140 | 0.9264 | 0.3357 | 0.4075 | 0.7871 | nan | 0.0 | 0.8811 | 0.8468 | 0.0 | nan | 0.6389 | 0.9125 | 0.0 | 0.0 | nan | 0.7963 | nan | 0.0 | nan | 0.0 | 0.6176 | 0.6285 | 0.0 | nan | 0.5777 | 0.8915 | 0.0 | 0.0 | nan | 0.6419 | nan | 0.0 |
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+ | 1.0194 | 8.42 | 160 | 0.8761 | 0.3499 | 0.4231 | 0.8038 | nan | 0.0 | 0.8549 | 0.8586 | 0.0 | nan | 0.7365 | 0.9299 | 0.0 | 0.0 | nan | 0.8508 | nan | 0.0 | nan | 0.0 | 0.6797 | 0.6342 | 0.0 | nan | 0.6119 | 0.8995 | 0.0 | 0.0 | nan | 0.6738 | nan | 0.0 |
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+ | 0.5558 | 9.47 | 180 | 0.8468 | 0.3458 | 0.4174 | 0.7981 | nan | 0.0 | 0.8533 | 0.8817 | 0.0 | nan | 0.6946 | 0.9063 | 0.0 | 0.0 | nan | 0.8381 | nan | 0.0 | nan | 0.0 | 0.6659 | 0.6338 | 0.0 | nan | 0.6155 | 0.8865 | 0.0 | 0.0 | nan | 0.6564 | nan | 0.0 |
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+ | 1.2579 | 10.53 | 200 | 0.7776 | 0.3502 | 0.4184 | 0.8047 | nan | 0.0 | 0.8678 | 0.8680 | 0.0 | nan | 0.6966 | 0.9388 | 0.0 | 0.0 | nan | 0.8131 | nan | 0.0 | nan | 0.0 | 0.6432 | 0.6556 | 0.0 | nan | 0.6191 | 0.8990 | 0.0 | 0.0 | nan | 0.6852 | nan | 0.0 |
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+ | 0.7671 | 11.58 | 220 | 0.7935 | 0.3579 | 0.4276 | 0.8152 | nan | 0.0 | 0.8816 | 0.8768 | 0.0 | nan | 0.7413 | 0.9356 | 0.0 | 0.0 | nan | 0.8410 | nan | 0.0 | nan | 0.0 | 0.6987 | 0.6610 | 0.0 | nan | 0.6315 | 0.9022 | 0.0 | 0.0 | nan | 0.6857 | nan | 0.0 |
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+ | 0.5097 | 12.63 | 240 | 0.7718 | 0.3549 | 0.4262 | 0.8129 | nan | 0.0 | 0.9047 | 0.8658 | 0.0 | nan | 0.7146 | 0.9298 | 0.0 | 0.0 | nan | 0.8467 | nan | 0.0 | nan | 0.0 | 0.6773 | 0.6707 | 0.0 | nan | 0.6172 | 0.9016 | 0.0 | 0.0 | nan | 0.6818 | nan | 0.0 |
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+ | 0.624 | 13.68 | 260 | 0.7270 | 0.3609 | 0.4282 | 0.8228 | nan | 0.0 | 0.8772 | 0.9219 | 0.0004 | nan | 0.7225 | 0.9308 | 0.0 | 0.0 | nan | 0.8291 | nan | 0.0 | nan | 0.0 | 0.7310 | 0.6897 | 0.0004 | nan | 0.5916 | 0.8975 | 0.0 | 0.0 | nan | 0.6988 | nan | 0.0 |
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+ | 0.535 | 14.74 | 280 | 0.7681 | 0.3526 | 0.4243 | 0.8085 | nan | 0.0 | 0.9574 | 0.8230 | 0.0009 | nan | 0.7059 | 0.9289 | 0.0 | 0.0 | nan | 0.8268 | nan | 0.0 | nan | 0.0 | 0.6786 | 0.6512 | 0.0009 | nan | 0.6011 | 0.9014 | 0.0 | 0.0 | nan | 0.6930 | nan | 0.0 |
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+ | 0.6093 | 15.79 | 300 | 0.6960 | 0.3636 | 0.4349 | 0.8257 | nan | 0.0 | 0.9296 | 0.8704 | 0.0102 | nan | 0.7227 | 0.9435 | 0.0 | 0.0 | nan | 0.8722 | nan | 0.0 | nan | 0.0 | 0.7270 | 0.6943 | 0.0102 | nan | 0.5991 | 0.9034 | 0.0 | 0.0 | nan | 0.7024 | nan | 0.0 |
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+ | 0.5584 | 16.84 | 320 | 0.6886 | 0.3671 | 0.4368 | 0.8281 | nan | 0.0 | 0.9186 | 0.8889 | 0.0157 | nan | 0.7333 | 0.9371 | 0.0 | 0.0 | nan | 0.8739 | nan | 0.0 | nan | 0.0 | 0.7428 | 0.6928 | 0.0157 | nan | 0.6008 | 0.9040 | 0.0 | 0.0 | nan | 0.7148 | nan | 0.0 |
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+ | 0.4421 | 17.89 | 340 | 0.6946 | 0.3644 | 0.4336 | 0.8238 | nan | 0.0 | 0.9061 | 0.8956 | 0.0308 | nan | 0.7280 | 0.9336 | 0.0 | 0.0 | nan | 0.8422 | nan | 0.0 | nan | 0.0 | 0.7217 | 0.6974 | 0.0308 | nan | 0.5717 | 0.9021 | 0.0 | 0.0 | nan | 0.7199 | nan | 0.0 |
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+ | 0.7997 | 18.95 | 360 | 0.7025 | 0.3580 | 0.4266 | 0.8172 | nan | 0.0 | 0.8983 | 0.8901 | 0.0075 | nan | 0.6955 | 0.9330 | 0.0 | 0.0 | nan | 0.8415 | nan | 0.0 | nan | 0.0 | 0.7140 | 0.6754 | 0.0075 | nan | 0.5592 | 0.9020 | 0.0 | 0.0 | nan | 0.7216 | nan | 0.0 |
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+ | 0.8388 | 20.0 | 380 | 0.6959 | 0.3632 | 0.4366 | 0.8242 | nan | 0.0 | 0.9513 | 0.8467 | 0.0120 | nan | 0.7460 | 0.9393 | 0.0 | 0.0 | nan | 0.8710 | nan | 0.0 | nan | 0.0 | 0.7218 | 0.6943 | 0.0120 | nan | 0.5799 | 0.9040 | 0.0 | 0.0 | nan | 0.7199 | nan | 0.0 |
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+ | 0.6424 | 21.05 | 400 | 0.6728 | 0.3651 | 0.4285 | 0.8280 | nan | 0.0 | 0.8680 | 0.9419 | 0.0007 | nan | 0.7148 | 0.9412 | 0.0 | 0.0 | nan | 0.8186 | nan | 0.0 | nan | 0.0 | 0.7527 | 0.6967 | 0.0007 | nan | 0.5737 | 0.9026 | 0.0 | 0.0 | nan | 0.7249 | nan | 0.0 |
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+ | 0.3287 | 22.11 | 420 | 0.6786 | 0.3621 | 0.4314 | 0.8247 | nan | 0.0 | 0.9357 | 0.8771 | 0.0053 | nan | 0.7122 | 0.9410 | 0.0 | 0.0 | nan | 0.8427 | nan | 0.0 | nan | 0.0 | 0.7335 | 0.6949 | 0.0053 | nan | 0.5626 | 0.9025 | 0.0 | 0.0 | nan | 0.7222 | nan | 0.0 |
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+ | 0.386 | 23.16 | 440 | 0.6603 | 0.3667 | 0.4354 | 0.8295 | nan | 0.0 | 0.9165 | 0.9030 | 0.0122 | nan | 0.7266 | 0.9361 | 0.0 | 0.0 | nan | 0.8593 | nan | 0.0 | nan | 0.0 | 0.7526 | 0.7050 | 0.0122 | nan | 0.5635 | 0.9033 | 0.0 | 0.0 | nan | 0.7301 | nan | 0.0 |
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+ | 0.3378 | 24.21 | 460 | 0.6791 | 0.3644 | 0.4331 | 0.8265 | nan | 0.0 | 0.9426 | 0.8772 | 0.0103 | nan | 0.7197 | 0.9405 | 0.0 | 0.0 | nan | 0.8403 | nan | 0.0 | nan | 0.0 | 0.7441 | 0.6939 | 0.0103 | nan | 0.5636 | 0.9039 | 0.0 | 0.0 | nan | 0.7284 | nan | 0.0 |
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+ | 0.3678 | 25.26 | 480 | 0.6915 | 0.3633 | 0.4342 | 0.8227 | nan | 0.0 | 0.9479 | 0.8577 | 0.0234 | nan | 0.7165 | 0.9384 | 0.0 | 0.0 | nan | 0.8579 | nan | 0.0 | nan | 0.0 | 0.7171 | 0.6910 | 0.0234 | nan | 0.5647 | 0.9051 | 0.0 | 0.0 | nan | 0.7320 | nan | 0.0 |
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+ | 0.328 | 26.32 | 500 | 0.6879 | 0.3662 | 0.4360 | 0.8259 | nan | 0.0 | 0.9434 | 0.8741 | 0.0266 | nan | 0.7189 | 0.9346 | 0.0 | 0.0 | nan | 0.8627 | nan | 0.0 | nan | 0.0 | 0.7357 | 0.6927 | 0.0266 | nan | 0.5712 | 0.9042 | 0.0 | 0.0 | nan | 0.7316 | nan | 0.0 |
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+ | 0.8502 | 27.37 | 520 | 0.6593 | 0.3644 | 0.4332 | 0.8270 | nan | 0.0 | 0.9414 | 0.8739 | 0.0066 | nan | 0.7263 | 0.9446 | 0.0 | 0.0 | nan | 0.8390 | nan | 0.0 | nan | 0.0 | 0.7449 | 0.6962 | 0.0066 | nan | 0.5647 | 0.9020 | 0.0 | 0.0 | nan | 0.7294 | nan | 0.0 |
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+ | 0.3528 | 28.42 | 540 | 0.6777 | 0.3626 | 0.4305 | 0.8238 | nan | 0.0 | 0.9439 | 0.8717 | 0.0114 | nan | 0.7046 | 0.9429 | 0.0 | 0.0 | nan | 0.8307 | nan | 0.0 | nan | 0.0 | 0.7364 | 0.6872 | 0.0114 | nan | 0.5563 | 0.9029 | 0.0 | 0.0 | nan | 0.7320 | nan | 0.0 |
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+ | 0.5908 | 29.47 | 560 | 0.6502 | 0.3640 | 0.4319 | 0.8283 | nan | 0.0 | 0.9324 | 0.8871 | 0.0017 | nan | 0.7226 | 0.9465 | 0.0 | 0.0 | nan | 0.8289 | nan | 0.0 | nan | 0.0 | 0.7525 | 0.7027 | 0.0017 | nan | 0.5584 | 0.8998 | 0.0 | 0.0 | nan | 0.7252 | nan | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.2
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+ - Pytorch 2.0.1
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+ - Datasets 2.15.0
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+ - Tokenizers 0.13.3
config.json ADDED
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+ {
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+ "_name_or_path": "nvidia/mit-b0",
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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": 256,
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+ "depths": [
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+ 2,
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+ ],
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+ 1,
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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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+ 32,
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+ 64,
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+ 160,
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+ 256
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+ ],
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+ "id2label": {
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+ "0": "unlabeled",
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+ "1": "object",
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+ "2": "road",
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+ "3": "plant",
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+ "4": "iron",
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+ "5": "wood",
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+ "6": "wall",
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+ "7": "raw_road",
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+ "8": "bottom_wall",
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+ "9": "roof",
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+ "10": "grass",
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+ "11": "mulch",
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+ "12": "person",
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+ "13": "Tomato"
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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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+ "Tomato": 13,
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+ "bottom_wall": 8,
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+ "grass": 10,
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+ "iron": 4,
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+ "mulch": 11,
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+ "object": 1,
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+ "person": 12,
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+ "plant": 3,
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+ "raw_road": 7,
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+ "road": 2,
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+ "roof": 9,
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+ "unlabeled": 0,
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+ "wall": 6,
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+ "wood": 5
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+ },
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+ "layer_norm_eps": 1e-06,
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+ "mlp_ratios": [
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+ 4,
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+ 4,
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+ 4,
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+ 4
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+ ],
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+ "model_type": "segformer",
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+ "num_attention_heads": [
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+ 1,
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+ 2,
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+ 5,
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+ 8
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+ ],
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+ "num_channels": 3,
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+ "num_encoder_blocks": 4,
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+ "patch_sizes": [
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+ 7,
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+ 3,
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+ 3,
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+ 3
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+ ],
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+ "reshape_last_stage": true,
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+ "semantic_loss_ignore_index": 255,
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+ "sr_ratios": [
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+ 8,
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+ 4,
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+ 2,
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+ 1
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+ ],
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+ "strides": [
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+ 4,
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.33.2"
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+ }
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