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testing_100_epoches

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segformer
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  1. README.md +114 -0
  2. config.json +78 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ base_model: nividia/mit-b2
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+ tags:
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+ - retouch
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: testing_100_epoches
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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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+ # testing_100_epoches
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+
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+ This model is a fine-tuned version of [nividia/mit-b2](https://huggingface.co/nividia/mit-b2) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1400
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+ - Mean Iou: 0.0066
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+ - Mean Accuracy: 0.0133
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+ - Overall Accuracy: 0.0133
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+ - Accuracy Bkg: nan
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+ - Accuracy Wht: 0.0133
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+ - Iou Bkg: 0.0
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+ - Iou Wht: 0.0133
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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: 3
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+ - eval_batch_size: 8
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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: 50
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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 Bkg | Accuracy Wht | Iou Bkg | Iou Wht |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:-------:|:-------:|
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+ | 0.1707 | 1.0 | 180 | 0.0800 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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+ | 0.0919 | 2.0 | 360 | 0.0798 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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+ | 0.0874 | 3.0 | 540 | 0.0771 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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+ | 0.0843 | 4.0 | 720 | 0.0786 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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+ | 0.0808 | 5.0 | 900 | 0.0805 | 0.0014 | 0.0029 | 0.0029 | nan | 0.0029 | 0.0 | 0.0029 |
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+ | 0.0775 | 6.0 | 1080 | 0.0796 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
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+ | 0.0712 | 7.0 | 1260 | 0.0906 | 0.0381 | 0.0762 | 0.0762 | nan | 0.0762 | 0.0 | 0.0762 |
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+ | 0.0629 | 8.0 | 1440 | 0.0830 | 0.0055 | 0.0110 | 0.0110 | nan | 0.0110 | 0.0 | 0.0110 |
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+ | 0.0547 | 9.0 | 1620 | 0.0911 | 0.0141 | 0.0281 | 0.0281 | nan | 0.0281 | 0.0 | 0.0281 |
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+ | 0.0483 | 10.0 | 1800 | 0.1032 | 0.0097 | 0.0194 | 0.0194 | nan | 0.0194 | 0.0 | 0.0194 |
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+ | 0.0443 | 11.0 | 1980 | 0.0924 | 0.0146 | 0.0292 | 0.0292 | nan | 0.0292 | 0.0 | 0.0292 |
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+ | 0.0406 | 12.0 | 2160 | 0.0979 | 0.0069 | 0.0137 | 0.0137 | nan | 0.0137 | 0.0 | 0.0137 |
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+ | 0.0369 | 13.0 | 2340 | 0.1030 | 0.0103 | 0.0206 | 0.0206 | nan | 0.0206 | 0.0 | 0.0206 |
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+ | 0.0352 | 14.0 | 2520 | 0.0993 | 0.0065 | 0.0129 | 0.0129 | nan | 0.0129 | 0.0 | 0.0129 |
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+ | 0.0341 | 15.0 | 2700 | 0.0978 | 0.0062 | 0.0125 | 0.0125 | nan | 0.0125 | 0.0 | 0.0125 |
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+ | 0.0324 | 16.0 | 2880 | 0.1044 | 0.0151 | 0.0302 | 0.0302 | nan | 0.0302 | 0.0 | 0.0302 |
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+ | 0.0307 | 17.0 | 3060 | 0.1014 | 0.0164 | 0.0328 | 0.0328 | nan | 0.0328 | 0.0 | 0.0328 |
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+ | 0.03 | 18.0 | 3240 | 0.1043 | 0.0128 | 0.0257 | 0.0257 | nan | 0.0257 | 0.0 | 0.0257 |
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+ | 0.0295 | 19.0 | 3420 | 0.1093 | 0.0083 | 0.0165 | 0.0165 | nan | 0.0165 | 0.0 | 0.0165 |
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+ | 0.0273 | 20.0 | 3600 | 0.1136 | 0.0100 | 0.0201 | 0.0201 | nan | 0.0201 | 0.0 | 0.0201 |
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+ | 0.0264 | 21.0 | 3780 | 0.1086 | 0.0154 | 0.0309 | 0.0309 | nan | 0.0309 | 0.0 | 0.0309 |
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+ | 0.0261 | 22.0 | 3960 | 0.1107 | 0.0165 | 0.0330 | 0.0330 | nan | 0.0330 | 0.0 | 0.0330 |
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+ | 0.0257 | 23.0 | 4140 | 0.1119 | 0.0137 | 0.0274 | 0.0274 | nan | 0.0274 | 0.0 | 0.0274 |
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+ | 0.0248 | 24.0 | 4320 | 0.1140 | 0.0101 | 0.0201 | 0.0201 | nan | 0.0201 | 0.0 | 0.0201 |
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+ | 0.0242 | 25.0 | 4500 | 0.1056 | 0.0168 | 0.0336 | 0.0336 | nan | 0.0336 | 0.0 | 0.0336 |
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+ | 0.024 | 26.0 | 4680 | 0.1143 | 0.0100 | 0.0200 | 0.0200 | nan | 0.0200 | 0.0 | 0.0200 |
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+ | 0.0234 | 27.0 | 4860 | 0.1155 | 0.0091 | 0.0181 | 0.0181 | nan | 0.0181 | 0.0 | 0.0181 |
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+ | 0.0228 | 28.0 | 5040 | 0.1201 | 0.0073 | 0.0146 | 0.0146 | nan | 0.0146 | 0.0 | 0.0146 |
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+ | 0.0226 | 29.0 | 5220 | 0.1192 | 0.0094 | 0.0188 | 0.0188 | nan | 0.0188 | 0.0 | 0.0188 |
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+ | 0.0224 | 30.0 | 5400 | 0.1187 | 0.0118 | 0.0237 | 0.0237 | nan | 0.0237 | 0.0 | 0.0237 |
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+ | 0.0218 | 31.0 | 5580 | 0.1227 | 0.0105 | 0.0209 | 0.0209 | nan | 0.0209 | 0.0 | 0.0209 |
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+ | 0.0211 | 32.0 | 5760 | 0.1159 | 0.0155 | 0.0310 | 0.0310 | nan | 0.0310 | 0.0 | 0.0310 |
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+ | 0.0208 | 33.0 | 5940 | 0.1224 | 0.0108 | 0.0215 | 0.0215 | nan | 0.0215 | 0.0 | 0.0215 |
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+ | 0.0203 | 34.0 | 6120 | 0.1239 | 0.0123 | 0.0246 | 0.0246 | nan | 0.0246 | 0.0 | 0.0246 |
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+ | 0.0197 | 35.0 | 6300 | 0.1285 | 0.0065 | 0.0130 | 0.0130 | nan | 0.0130 | 0.0 | 0.0130 |
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+ | 0.02 | 36.0 | 6480 | 0.1293 | 0.0037 | 0.0075 | 0.0075 | nan | 0.0075 | 0.0 | 0.0075 |
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+ | 0.0192 | 37.0 | 6660 | 0.1258 | 0.0059 | 0.0119 | 0.0119 | nan | 0.0119 | 0.0 | 0.0119 |
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+ | 0.0193 | 38.0 | 6840 | 0.1234 | 0.0105 | 0.0210 | 0.0210 | nan | 0.0210 | 0.0 | 0.0210 |
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+ | 0.0189 | 39.0 | 7020 | 0.1267 | 0.0080 | 0.0159 | 0.0159 | nan | 0.0159 | 0.0 | 0.0159 |
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+ | 0.0181 | 40.0 | 7200 | 0.1308 | 0.0060 | 0.0120 | 0.0120 | nan | 0.0120 | 0.0 | 0.0120 |
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+ | 0.0183 | 41.0 | 7380 | 0.1337 | 0.0056 | 0.0112 | 0.0112 | nan | 0.0112 | 0.0 | 0.0112 |
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+ | 0.018 | 42.0 | 7560 | 0.1349 | 0.0071 | 0.0142 | 0.0142 | nan | 0.0142 | 0.0 | 0.0142 |
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+ | 0.0178 | 43.0 | 7740 | 0.1332 | 0.0069 | 0.0139 | 0.0139 | nan | 0.0139 | 0.0 | 0.0139 |
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+ | 0.0171 | 44.0 | 7920 | 0.1363 | 0.0066 | 0.0132 | 0.0132 | nan | 0.0132 | 0.0 | 0.0132 |
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+ | 0.0176 | 45.0 | 8100 | 0.1352 | 0.0065 | 0.0131 | 0.0131 | nan | 0.0131 | 0.0 | 0.0131 |
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+ | 0.0181 | 46.0 | 8280 | 0.1384 | 0.0064 | 0.0127 | 0.0127 | nan | 0.0127 | 0.0 | 0.0127 |
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+ | 0.0173 | 47.0 | 8460 | 0.1419 | 0.0065 | 0.0129 | 0.0129 | nan | 0.0129 | 0.0 | 0.0129 |
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+ | 0.0176 | 48.0 | 8640 | 0.1374 | 0.0081 | 0.0161 | 0.0161 | nan | 0.0161 | 0.0 | 0.0161 |
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+ | 0.0173 | 49.0 | 8820 | 0.1383 | 0.0065 | 0.0130 | 0.0130 | nan | 0.0130 | 0.0 | 0.0130 |
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+ | 0.0173 | 50.0 | 9000 | 0.1400 | 0.0066 | 0.0133 | 0.0133 | nan | 0.0133 | 0.0 | 0.0133 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
config.json ADDED
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+ {
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+ "_name_or_path": "nvidia/mit-b2",
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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": "BKG",
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+ "1": "WHT"
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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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+ "BKG": 0,
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+ "WHT": 1
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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": "4.33.3"
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+ }
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