more augs
Browse files- README.md +39 -94
- config.json +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [nancyalarabawy/segformer-finetuned-segments-plantleafdisease-may-25](https://huggingface.co/nancyalarabawy/segformer-finetuned-segments-plantleafdisease-may-25) on the all plus aug dataset.
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It achieves the following results on the evaluation set:
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- optimizer: Use 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: cosine
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- lr_scheduler_warmup_steps: 200
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Healthy | Accuracy Blast | Accuracy Background | Accuracy Alstonia herpes | Accuracy Mango anthracnose | Accuracy Pomengrate spots | Accuracy Blight spots | Accuracy Chlorisis | Accuracy Leaf spots | Accuracy Apple scab | Accuracy Black rot | Accuracy Cedar rust | Iou Unlabeled | Iou Healthy | Iou Blast | Iou Background | Iou Alstonia herpes | Iou Mango anthracnose | Iou Pomengrate spots | Iou Blight spots | Iou Chlorisis | Iou Leaf spots | Iou Apple scab | Iou Black rot | Iou Cedar rust |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:--------------:|:-------------------:|:------------------------:|:--------------------------:|:-------------------------:|:---------------------:|:------------------:|:-------------------:|:-------------------:|:------------------:|:-------------------:|:-------------:|:-----------:|:---------:|:--------------:|:-------------------:|:---------------------:|:--------------------:|:----------------:|:-------------:|:--------------:|:--------------:|:-------------:|:--------------:|
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| 0.0247 | 0.1828 | 100 | 0.0364 | 0.5756 | 0.7103 | 0.9892 | nan | 0.9814 | 0.8186 | 0.9969 | 0.6262 | 0.6237 | 0.5668 | 0.0 | 0.7794 | 0.7509 | 0.8196 | 0.9023 | 0.6580 | 0.0 | 0.9606 | 0.6983 | 0.9941 | 0.5942 | 0.4725 | 0.5619 | 0.0 | 0.6211 | 0.5030 | 0.6947 | 0.8117 | 0.5704 |
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| 0.0399 | 0.3656 | 200 | 0.0334 | 0.5989 | 0.7213 | 0.9896 | nan | 0.9843 | 0.8546 | 0.9975 | 0.8743 | 0.5544 | 0.8649 | 0.1653 | 0.5090 | 0.6551 | 0.7978 | 0.8766 | 0.5217 | 0.0 | 0.9612 | 0.7199 | 0.9945 | 0.6440 | 0.4801 | 0.8130 | 0.1514 | 0.5003 | 0.5220 | 0.6993 | 0.8171 | 0.4836 |
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| 0.0308 | 0.5484 | 300 | 0.0315 | 0.6154 | 0.7233 | 0.9898 | nan | 0.9867 | 0.8030 | 0.9972 | 0.7148 | 0.6492 | 0.8778 | 0.1216 | 0.6532 | 0.6556 | 0.6762 | 0.9013 | 0.6435 | 0.0 | 0.9627 | 0.7113 | 0.9943 | 0.6575 | 0.5272 | 0.8237 | 0.1158 | 0.6146 | 0.5661 | 0.6366 | 0.8223 | 0.5677 |
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| 0.0223 | 0.7313 | 400 | 0.0334 | 0.6229 | 0.7657 | 0.9900 | nan | 0.9872 | 0.8189 | 0.9968 | 0.8255 | 0.6690 | 0.8962 | 0.4606 | 0.7488 | 0.5116 | 0.7323 | 0.9053 | 0.6365 | 0.0 | 0.9631 | 0.7187 | 0.9946 | 0.6756 | 0.5203 | 0.8148 | 0.2197 | 0.6611 | 0.4674 | 0.6743 | 0.8237 | 0.5649 |
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| 0.0143 | 0.9141 | 500 | 0.0322 | 0.6345 | 0.7599 | 0.9900 | nan | 0.9827 | 0.8081 | 0.9973 | 0.7908 | 0.6231 | 0.8966 | 0.2256 | 0.7685 | 0.6073 | 0.8548 | 0.9028 | 0.6607 | 0.0 | 0.9625 | 0.7172 | 0.9943 | 0.6922 | 0.5198 | 0.8181 | 0.2104 | 0.6975 | 0.5345 | 0.7051 | 0.8252 | 0.5722 |
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| 0.0231 | 1.0969 | 600 | 0.0300 | 0.6478 | 0.7988 | 0.9902 | nan | 0.9828 | 0.8272 | 0.9970 | 0.8397 | 0.7685 | 0.9001 | 0.4121 | 0.7727 | 0.6754 | 0.8823 | 0.8901 | 0.6379 | 0.0 | 0.9633 | 0.7232 | 0.9945 | 0.6943 | 0.5623 | 0.8204 | 0.2696 | 0.7264 | 0.5733 | 0.7051 | 0.8224 | 0.5672 |
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| 0.0357 | 1.2797 | 700 | 0.0310 | 0.6275 | 0.7904 | 0.9899 | nan | 0.9818 | 0.8255 | 0.9969 | 0.8079 | 0.6468 | 0.9251 | 0.2804 | 0.7830 | 0.8433 | 0.8515 | 0.9238 | 0.6186 | 0.0 | 0.9626 | 0.7167 | 0.9946 | 0.6718 | 0.5228 | 0.8032 | 0.2057 | 0.6918 | 0.5152 | 0.7138 | 0.8080 | 0.5519 |
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| 0.0264 | 1.4625 | 800 | 0.0294 | 0.6527 | 0.7939 | 0.9905 | nan | 0.9843 | 0.7832 | 0.9975 | 0.7592 | 0.6609 | 0.9459 | 0.5303 | 0.7706 | 0.7476 | 0.8375 | 0.8954 | 0.6150 | 0.0 | 0.9646 | 0.7107 | 0.9946 | 0.6874 | 0.5622 | 0.8180 | 0.3383 | 0.7054 | 0.5998 | 0.7208 | 0.8259 | 0.5580 |
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| 0.0141 | 1.6453 | 900 | 0.0313 | 0.6333 | 0.7409 | 0.9898 | nan | 0.9895 | 0.7194 | 0.9969 | 0.7358 | 0.5662 | 0.8744 | 0.4443 | 0.8467 | 0.6098 | 0.6006 | 0.8675 | 0.6398 | 0.0 | 0.9617 | 0.6698 | 0.9946 | 0.6724 | 0.5066 | 0.8253 | 0.3576 | 0.7215 | 0.5565 | 0.5824 | 0.8160 | 0.5680 |
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| 0.0371 | 1.8282 | 1000 | 0.0325 | 0.6531 | 0.7737 | 0.9905 | nan | 0.9871 | 0.7816 | 0.9968 | 0.7485 | 0.6003 | 0.8950 | 0.4087 | 0.8286 | 0.7537 | 0.8086 | 0.8740 | 0.6014 | 0.0 | 0.9642 | 0.7055 | 0.9946 | 0.6836 | 0.5341 | 0.8258 | 0.3517 | 0.7331 | 0.6217 | 0.7062 | 0.8186 | 0.5505 |
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| 0.0199 | 2.0110 | 1100 | 0.0309 | 0.6501 | 0.8027 | 0.9899 | nan | 0.9824 | 0.8095 | 0.9975 | 0.8447 | 0.7098 | 0.9227 | 0.4682 | 0.9273 | 0.7420 | 0.7844 | 0.9243 | 0.5190 | 0.0 | 0.9624 | 0.7116 | 0.9945 | 0.6940 | 0.5620 | 0.8296 | 0.3912 | 0.6876 | 0.6184 | 0.7023 | 0.8098 | 0.4879 |
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| 0.0305 | 2.1938 | 1200 | 0.0323 | 0.6503 | 0.7788 | 0.9895 | nan | 0.9858 | 0.7525 | 0.9968 | 0.8399 | 0.6918 | 0.9356 | 0.4931 | 0.7528 | 0.7325 | 0.6997 | 0.8969 | 0.5679 | 0.0 | 0.9624 | 0.6817 | 0.9938 | 0.7076 | 0.5652 | 0.8303 | 0.4262 | 0.6667 | 0.6160 | 0.6578 | 0.8235 | 0.5223 |
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| 0.0303 | 2.3766 | 1300 | 0.0315 | 0.6676 | 0.8197 | 0.9903 | nan | 0.9840 | 0.7869 | 0.9966 | 0.8387 | 0.6857 | 0.9170 | 0.5436 | 0.9241 | 0.6873 | 0.8511 | 0.9099 | 0.7114 | 0.0 | 0.9637 | 0.7061 | 0.9946 | 0.7059 | 0.5703 | 0.8374 | 0.4359 | 0.7342 | 0.6124 | 0.7107 | 0.8243 | 0.5836 |
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| 0.0279 | 2.5594 | 1400 | 0.0287 | 0.6344 | 0.7766 | 0.9900 | nan | 0.9845 | 0.8062 | 0.9969 | 0.8714 | 0.7185 | 0.8172 | 0.3016 | 0.7949 | 0.7289 | 0.7897 | 0.9063 | 0.6028 | 0.0 | 0.9628 | 0.7051 | 0.9945 | 0.6878 | 0.5410 | 0.7605 | 0.2731 | 0.6767 | 0.5845 | 0.7006 | 0.8215 | 0.5393 |
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| 0.0215 | 2.7422 | 1500 | 0.0298 | 0.6559 | 0.7916 | 0.9902 | nan | 0.9861 | 0.7679 | 0.9970 | 0.8481 | 0.6928 | 0.9053 | 0.5003 | 0.8564 | 0.7083 | 0.7676 | 0.8685 | 0.6004 | 0.0 | 0.9639 | 0.7003 | 0.9944 | 0.7032 | 0.5644 | 0.8343 | 0.4250 | 0.6920 | 0.5882 | 0.6971 | 0.8188 | 0.5454 |
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| 0.0318 | 2.9250 | 1600 | 0.0307 | 0.6611 | 0.7989 | 0.9906 | nan | 0.9840 | 0.8401 | 0.9977 | 0.8163 | 0.6231 | 0.9191 | 0.5393 | 0.8432 | 0.6991 | 0.7947 | 0.9079 | 0.6224 | 0.0 | 0.9645 | 0.7294 | 0.9946 | 0.6974 | 0.5410 | 0.8269 | 0.3994 | 0.7408 | 0.6032 | 0.7111 | 0.8251 | 0.5605 |
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| 0.0304 | 3.1079 | 1700 | 0.0303 | 0.6590 | 0.7815 | 0.9905 | nan | 0.9868 | 0.7813 | 0.9971 | 0.8347 | 0.6602 | 0.9049 | 0.4367 | 0.6893 | 0.7834 | 0.7770 | 0.8969 | 0.6300 | 0.0 | 0.9644 | 0.7122 | 0.9947 | 0.6947 | 0.5656 | 0.8353 | 0.4102 | 0.6673 | 0.6309 | 0.7027 | 0.8245 | 0.5645 |
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| 0.0141 | 3.2907 | 1800 | 0.0287 | 0.6655 | 0.7933 | 0.9907 | nan | 0.9873 | 0.7558 | 0.9970 | 0.8028 | 0.6391 | 0.9224 | 0.5102 | 0.8238 | 0.7741 | 0.7842 | 0.8932 | 0.6301 | 0.0 | 0.9651 | 0.6971 | 0.9947 | 0.6928 | 0.5630 | 0.8314 | 0.4131 | 0.7577 | 0.6363 | 0.7079 | 0.8250 | 0.5670 |
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| 0.0296 | 3.4735 | 1900 | 0.0293 | 0.6687 | 0.7956 | 0.9907 | nan | 0.9857 | 0.8179 | 0.9974 | 0.7915 | 0.6124 | 0.9189 | 0.5849 | 0.8551 | 0.6689 | 0.8051 | 0.8822 | 0.6275 | 0.0 | 0.9649 | 0.7212 | 0.9947 | 0.7057 | 0.5403 | 0.8387 | 0.4712 | 0.7483 | 0.6042 | 0.7138 | 0.8223 | 0.5678 |
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| 0.0223 | 3.6563 | 2000 | 0.0288 | 0.6653 | 0.7827 | 0.9907 | nan | 0.9866 | 0.8164 | 0.9973 | 0.7565 | 0.6088 | 0.9136 | 0.4884 | 0.8564 | 0.6591 | 0.7812 | 0.8926 | 0.6349 | 0.0 | 0.9652 | 0.7237 | 0.9948 | 0.6882 | 0.5439 | 0.8402 | 0.4482 | 0.7463 | 0.5964 | 0.7059 | 0.8257 | 0.5706 |
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| 0.0134 | 3.8391 | 2100 | 0.0313 | 0.6608 | 0.8067 | 0.9904 | nan | 0.9853 | 0.8569 | 0.9969 | 0.8114 | 0.6977 | 0.9359 | 0.5261 | 0.8627 | 0.7381 | 0.8027 | 0.7746 | 0.6923 | 0.0 | 0.9648 | 0.7360 | 0.9947 | 0.6720 | 0.5647 | 0.8285 | 0.4428 | 0.7624 | 0.6243 | 0.7098 | 0.7409 | 0.5502 |
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| 0.0159 | 4.0219 | 2200 | 0.0275 | 0.6721 | 0.8063 | 0.9907 | nan | 0.9843 | 0.8433 | 0.9975 | 0.8296 | 0.6645 | 0.9222 | 0.4954 | 0.8599 | 0.7104 | 0.7976 | 0.8999 | 0.6712 | 0.0 | 0.9651 | 0.7372 | 0.9947 | 0.6966 | 0.5691 | 0.8381 | 0.4273 | 0.7616 | 0.6262 | 0.7127 | 0.8260 | 0.5833 |
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| 0.0205 | 4.2048 | 2300 | 0.0284 | 0.6800 | 0.8227 | 0.9907 | nan | 0.9833 | 0.8650 | 0.9975 | 0.8534 | 0.6993 | 0.9182 | 0.5503 | 0.8645 | 0.7430 | 0.8067 | 0.9035 | 0.6881 | 0.0 | 0.9653 | 0.7368 | 0.9946 | 0.6971 | 0.5847 | 0.8402 | 0.4826 | 0.7598 | 0.6471 | 0.7153 | 0.8260 | 0.5900 |
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| 0.0134 | 4.3876 | 2400 | 0.0287 | 0.6778 | 0.8223 | 0.9907 | nan | 0.9853 | 0.8345 | 0.9970 | 0.8358 | 0.7680 | 0.9380 | 0.5359 | 0.8578 | 0.7442 | 0.8074 | 0.9084 | 0.6557 | 0.0 | 0.9654 | 0.7368 | 0.9947 | 0.7002 | 0.5963 | 0.8314 | 0.4760 | 0.7548 | 0.6342 | 0.7151 | 0.8246 | 0.5817 |
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| 0.017 | 4.5704 | 2500 | 0.0286 | 0.6806 | 0.8342 | 0.9908 | nan | 0.9831 | 0.8589 | 0.9973 | 0.8352 | 0.7004 | 0.9285 | 0.5474 | 0.8904 | 0.8124 | 0.8095 | 0.9097 | 0.7379 | 0.0 | 0.9654 | 0.7409 | 0.9948 | 0.7026 | 0.5807 | 0.8417 | 0.4748 | 0.7478 | 0.6568 | 0.7149 | 0.8249 | 0.6023 |
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| 0.043 | 4.7532 | 2600 | 0.0284 | 0.6795 | 0.8210 | 0.9908 | nan | 0.9847 | 0.8138 | 0.9973 | 0.8324 | 0.7019 | 0.9349 | 0.5532 | 0.8787 | 0.7561 | 0.8252 | 0.9062 | 0.6681 | 0.0 | 0.9656 | 0.7343 | 0.9948 | 0.7101 | 0.5875 | 0.8356 | 0.4800 | 0.7515 | 0.6415 | 0.7166 | 0.8269 | 0.5892 |
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| 0.019 | 4.9360 | 2700 | 0.0287 | 0.6672 | 0.7930 | 0.9907 | nan | 0.9860 | 0.8205 | 0.9975 | 0.8003 | 0.5501 | 0.9166 | 0.5845 | 0.8431 | 0.7223 | 0.7533 | 0.9031 | 0.6384 | 0.0 | 0.9649 | 0.7308 | 0.9948 | 0.6875 | 0.5098 | 0.8368 | 0.4837 | 0.7445 | 0.6346 | 0.6914 | 0.8264 | 0.5687 |
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| 0.0188 | 5.1188 | 2800 | 0.0291 | 0.6806 | 0.8241 | 0.9907 | nan | 0.9845 | 0.8383 | 0.9971 | 0.8324 | 0.6769 | 0.9406 | 0.6156 | 0.8697 | 0.7783 | 0.8267 | 0.9074 | 0.6215 | 0.0 | 0.9656 | 0.7434 | 0.9945 | 0.6971 | 0.5778 | 0.8330 | 0.5192 | 0.7575 | 0.6474 | 0.7194 | 0.8277 | 0.5656 |
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| 0.1478 | 5.3016 | 2900 | 0.0283 | 0.6789 | 0.8109 | 0.9909 | nan | 0.9855 | 0.8052 | 0.9972 | 0.8235 | 0.6226 | 0.9200 | 0.5546 | 0.8629 | 0.7570 | 0.8281 | 0.8876 | 0.6870 | 0.0 | 0.9658 | 0.7323 | 0.9949 | 0.6978 | 0.5486 | 0.8440 | 0.4984 | 0.7608 | 0.6462 | 0.7197 | 0.8256 | 0.5915 |
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| 0.0212 | 5.4845 | 3000 | 0.0289 | 0.6787 | 0.8162 | 0.9908 | nan | 0.9851 | 0.7957 | 0.9973 | 0.8042 | 0.6644 | 0.9449 | 0.6075 | 0.8410 | 0.7493 | 0.8221 | 0.9125 | 0.6710 | 0.0 | 0.9654 | 0.7271 | 0.9948 | 0.6964 | 0.5737 | 0.8272 | 0.5209 | 0.7435 | 0.6497 | 0.7151 | 0.8247 | 0.5843 |
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| 0.0224 | 5.6673 | 3100 | 0.0286 | 0.6843 | 0.8257 | 0.9909 | nan | 0.9853 | 0.8130 | 0.9973 | 0.8811 | 0.7025 | 0.9269 | 0.6293 | 0.8518 | 0.7494 | 0.8163 | 0.9034 | 0.6519 | 0.0 | 0.9657 | 0.7344 | 0.9949 | 0.6937 | 0.5865 | 0.8416 | 0.5524 | 0.7522 | 0.6525 | 0.7164 | 0.8266 | 0.5797 |
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| 0.0157 | 5.8501 | 3200 | 0.0296 | 0.6846 | 0.8216 | 0.9909 | nan | 0.9862 | 0.8265 | 0.9973 | 0.8326 | 0.6679 | 0.9329 | 0.6645 | 0.8475 | 0.7788 | 0.7715 | 0.9008 | 0.6531 | 0.0 | 0.9658 | 0.7404 | 0.9949 | 0.6981 | 0.5742 | 0.8404 | 0.5722 | 0.7534 | 0.6481 | 0.7025 | 0.8276 | 0.5816 |
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| 0.0154 | 6.0329 | 3300 | 0.0285 | 0.6826 | 0.8208 | 0.9910 | nan | 0.9855 | 0.8110 | 0.9973 | 0.8601 | 0.6744 | 0.9342 | 0.5859 | 0.8757 | 0.7503 | 0.8071 | 0.8948 | 0.6734 | 0.0 | 0.9658 | 0.7363 | 0.9949 | 0.6989 | 0.5746 | 0.8400 | 0.5245 | 0.7693 | 0.6397 | 0.7145 | 0.8266 | 0.5885 |
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| 0.0166 | 6.2157 | 3400 | 0.0280 | 0.6877 | 0.8250 | 0.9909 | nan | 0.9848 | 0.8424 | 0.9975 | 0.8446 | 0.6461 | 0.9314 | 0.6832 | 0.8845 | 0.7095 | 0.8192 | 0.9021 | 0.6550 | 0.0 | 0.9657 | 0.7457 | 0.9949 | 0.7024 | 0.5653 | 0.8425 | 0.5919 | 0.7655 | 0.6397 | 0.7165 | 0.8274 | 0.5820 |
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| 0.013 | 6.3985 | 3500 | 0.0279 | 0.6925 | 0.8386 | 0.9911 | nan | 0.9853 | 0.8384 | 0.9972 | 0.8352 | 0.7261 | 0.9340 | 0.6947 | 0.8683 | 0.8013 | 0.8212 | 0.9019 | 0.6594 | 0.0 | 0.9662 | 0.7459 | 0.9949 | 0.7025 | 0.5998 | 0.8421 | 0.5860 | 0.7634 | 0.6689 | 0.7197 | 0.8271 | 0.5868 |
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| 0.0149 | 6.5814 | 3600 | 0.0282 | 0.6856 | 0.8202 | 0.9910 | nan | 0.9851 | 0.8400 | 0.9974 | 0.8269 | 0.7202 | 0.9228 | 0.5916 | 0.8214 | 0.7446 | 0.8221 | 0.9046 | 0.6653 | 0.0 | 0.9661 | 0.7458 | 0.9949 | 0.7005 | 0.5914 | 0.8442 | 0.5369 | 0.7369 | 0.6558 | 0.7204 | 0.8279 | 0.5915 |
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| 0.0207 | 6.7642 | 3700 | 0.0287 | 0.6901 | 0.8315 | 0.9911 | nan | 0.9853 | 0.8422 | 0.9972 | 0.8180 | 0.7382 | 0.9291 | 0.6309 | 0.8638 | 0.7648 | 0.8227 | 0.9024 | 0.6829 | 0.0 | 0.9662 | 0.7474 | 0.9949 | 0.7004 | 0.5976 | 0.8443 | 0.5560 | 0.7520 | 0.6688 | 0.7209 | 0.8275 | 0.5953 |
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| 0.0138 | 6.9470 | 3800 | 0.0289 | 0.6870 | 0.8220 | 0.9910 | nan | 0.9858 | 0.8377 | 0.9973 | 0.8254 | 0.6805 | 0.9271 | 0.6084 | 0.8649 | 0.7528 | 0.8061 | 0.9088 | 0.6690 | 0.0 | 0.9661 | 0.7479 | 0.9949 | 0.7004 | 0.5834 | 0.8443 | 0.5493 | 0.7473 | 0.6640 | 0.7164 | 0.8273 | 0.5896 |
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| 0.0135 | 7.1298 | 3900 | 0.0288 | 0.6895 | 0.8258 | 0.9911 | nan | 0.9853 | 0.8233 | 0.9975 | 0.8234 | 0.6803 | 0.9270 | 0.6601 | 0.8736 | 0.7546 | 0.8119 | 0.9025 | 0.6705 | 0.0 | 0.9662 | 0.7433 | 0.9949 | 0.6992 | 0.5817 | 0.8461 | 0.5767 | 0.7523 | 0.6658 | 0.7189 | 0.8277 | 0.5907 |
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| 121 |
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| 0.0251 | 7.3126 | 4000 | 0.0288 | 0.6896 | 0.8267 | 0.9911 | nan | 0.9850 | 0.8260 | 0.9976 | 0.8249 | 0.7056 | 0.9297 | 0.6779 | 0.8595 | 0.7520 | 0.8180 | 0.9013 | 0.6431 | 0.0 | 0.9662 | 0.7446 | 0.9949 | 0.7012 | 0.5894 | 0.8441 | 0.5850 | 0.7504 | 0.6615 | 0.7204 | 0.8274 | 0.5798 |
|
| 122 |
-
| 0.0126 | 7.4954 | 4100 | 0.0287 | 0.6889 | 0.8280 | 0.9910 | nan | 0.9855 | 0.8399 | 0.9972 | 0.8361 | 0.6770 | 0.9363 | 0.6507 | 0.8664 | 0.7517 | 0.8195 | 0.9095 | 0.6660 | 0.0 | 0.9661 | 0.7488 | 0.9949 | 0.7023 | 0.5784 | 0.8424 | 0.5765 | 0.7516 | 0.6578 | 0.7199 | 0.8274 | 0.5895 |
|
| 123 |
-
| 0.0157 | 7.6782 | 4200 | 0.0286 | 0.6911 | 0.8329 | 0.9911 | nan | 0.9852 | 0.8372 | 0.9973 | 0.8510 | 0.6997 | 0.9312 | 0.6633 | 0.8717 | 0.7635 | 0.8203 | 0.9067 | 0.6679 | 0.0 | 0.9663 | 0.7489 | 0.9949 | 0.7016 | 0.5875 | 0.8439 | 0.5812 | 0.7568 | 0.6641 | 0.7209 | 0.8277 | 0.5907 |
|
| 124 |
-
| 0.0192 | 7.8611 | 4300 | 0.0287 | 0.6862 | 0.8133 | 0.9911 | nan | 0.9868 | 0.8190 | 0.9972 | 0.8135 | 0.6758 | 0.9149 | 0.6083 | 0.8642 | 0.7331 | 0.7930 | 0.8948 | 0.6588 | 0.0 | 0.9662 | 0.7438 | 0.9949 | 0.7017 | 0.5803 | 0.8445 | 0.5526 | 0.7542 | 0.6548 | 0.7131 | 0.8269 | 0.5878 |
|
| 125 |
-
| 0.0171 | 8.0439 | 4400 | 0.0285 | 0.6886 | 0.8230 | 0.9911 | nan | 0.9855 | 0.8363 | 0.9975 | 0.8392 | 0.7020 | 0.9258 | 0.6232 | 0.8750 | 0.7400 | 0.8056 | 0.8968 | 0.6489 | 0.0 | 0.9663 | 0.7503 | 0.9949 | 0.7059 | 0.5883 | 0.8451 | 0.5587 | 0.7568 | 0.6558 | 0.7171 | 0.8276 | 0.5844 |
|
| 126 |
-
| 0.0198 | 8.2267 | 4500 | 0.0286 | 0.6891 | 0.8249 | 0.9911 | nan | 0.9854 | 0.8532 | 0.9975 | 0.8317 | 0.6888 | 0.9235 | 0.6431 | 0.8651 | 0.7497 | 0.8042 | 0.9025 | 0.6535 | 0.0 | 0.9663 | 0.7533 | 0.9949 | 0.7044 | 0.5851 | 0.8453 | 0.5653 | 0.7555 | 0.6579 | 0.7166 | 0.8279 | 0.5864 |
|
| 127 |
-
| 0.0127 | 8.4095 | 4600 | 0.0287 | 0.6888 | 0.8227 | 0.9911 | nan | 0.9858 | 0.8219 | 0.9974 | 0.8190 | 0.6709 | 0.9260 | 0.6548 | 0.8747 | 0.7509 | 0.8210 | 0.8994 | 0.6503 | 0.0 | 0.9663 | 0.7457 | 0.9949 | 0.7017 | 0.5786 | 0.8452 | 0.5726 | 0.7578 | 0.6581 | 0.7210 | 0.8276 | 0.5849 |
|
| 128 |
-
| 0.0114 | 8.5923 | 4700 | 0.0287 | 0.6902 | 0.8269 | 0.9911 | nan | 0.9858 | 0.8406 | 0.9974 | 0.8322 | 0.6868 | 0.9289 | 0.6708 | 0.8727 | 0.7451 | 0.8087 | 0.9038 | 0.6501 | 0.0 | 0.9663 | 0.7513 | 0.9949 | 0.7031 | 0.5837 | 0.8443 | 0.5830 | 0.7574 | 0.6574 | 0.7181 | 0.8281 | 0.5852 |
|
| 129 |
-
| 0.0211 | 8.7751 | 4800 | 0.0286 | 0.6898 | 0.8271 | 0.9911 | nan | 0.9856 | 0.8525 | 0.9974 | 0.8345 | 0.6684 | 0.9287 | 0.6631 | 0.8732 | 0.7529 | 0.8114 | 0.9025 | 0.6547 | 0.0 | 0.9663 | 0.7538 | 0.9949 | 0.7030 | 0.5774 | 0.8442 | 0.5791 | 0.7570 | 0.6584 | 0.7187 | 0.8277 | 0.5870 |
|
| 130 |
-
| 0.0152 | 8.9580 | 4900 | 0.0285 | 0.6902 | 0.8269 | 0.9911 | nan | 0.9855 | 0.8400 | 0.9974 | 0.8324 | 0.6878 | 0.9311 | 0.6516 | 0.8774 | 0.7486 | 0.8134 | 0.8981 | 0.6594 | 0.0 | 0.9664 | 0.7514 | 0.9949 | 0.7034 | 0.5849 | 0.8436 | 0.5771 | 0.7582 | 0.6574 | 0.7193 | 0.8275 | 0.5890 |
|
| 131 |
-
| 0.022 | 9.1408 | 5000 | 0.0285 | 0.6910 | 0.8274 | 0.9911 | nan | 0.9856 | 0.8393 | 0.9974 | 0.8309 | 0.6972 | 0.9260 | 0.6669 | 0.8726 | 0.7482 | 0.8060 | 0.9003 | 0.6588 | 0.0 | 0.9664 | 0.7510 | 0.9949 | 0.7040 | 0.5879 | 0.8450 | 0.5834 | 0.7584 | 0.6584 | 0.7173 | 0.8278 | 0.5888 |
|
| 132 |
-
| 0.0128 | 9.3236 | 5100 | 0.0285 | 0.6906 | 0.8252 | 0.9911 | nan | 0.9858 | 0.8385 | 0.9974 | 0.8313 | 0.6902 | 0.9234 | 0.6558 | 0.8704 | 0.7451 | 0.8107 | 0.8982 | 0.6558 | 0.0 | 0.9664 | 0.7509 | 0.9949 | 0.7039 | 0.5859 | 0.8457 | 0.5802 | 0.7582 | 0.6574 | 0.7186 | 0.8277 | 0.5876 |
|
| 133 |
-
| 0.0229 | 9.5064 | 5200 | 0.0286 | 0.6919 | 0.8303 | 0.9911 | nan | 0.9855 | 0.8424 | 0.9974 | 0.8360 | 0.6935 | 0.9274 | 0.6806 | 0.8769 | 0.7510 | 0.8154 | 0.8997 | 0.6575 | 0.0 | 0.9664 | 0.7518 | 0.9949 | 0.7043 | 0.5870 | 0.8449 | 0.5914 | 0.7588 | 0.6599 | 0.7197 | 0.8277 | 0.5886 |
|
| 134 |
-
| 0.0139 | 9.6892 | 5300 | 0.0285 | 0.6915 | 0.8293 | 0.9911 | nan | 0.9854 | 0.8423 | 0.9974 | 0.8340 | 0.6919 | 0.9298 | 0.6672 | 0.8762 | 0.7505 | 0.8147 | 0.9019 | 0.6604 | 0.0 | 0.9664 | 0.7517 | 0.9949 | 0.7046 | 0.5867 | 0.8443 | 0.5858 | 0.7588 | 0.6594 | 0.7197 | 0.8278 | 0.5898 |
|
| 135 |
-
| 0.0194 | 9.8720 | 5400 | 0.0285 | 0.6918 | 0.8323 | 0.9911 | nan | 0.9853 | 0.8445 | 0.9974 | 0.8414 | 0.6928 | 0.9298 | 0.6815 | 0.8781 | 0.7542 | 0.8181 | 0.9040 | 0.6603 | 0.0 | 0.9664 | 0.7522 | 0.9949 | 0.7043 | 0.5863 | 0.8442 | 0.5889 | 0.7587 | 0.6600 | 0.7201 | 0.8280 | 0.5898 |
|
| 136 |
-
|
| 137 |
|
| 138 |
### Framework versions
|
| 139 |
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|
|
| 18 |
|
| 19 |
This model is a fine-tuned version of [nancyalarabawy/segformer-finetuned-segments-plantleafdisease-may-25](https://huggingface.co/nancyalarabawy/segformer-finetuned-segments-plantleafdisease-may-25) on the all plus aug dataset.
|
| 20 |
It achieves the following results on the evaluation set:
|
| 21 |
+
- eval_loss: 0.0277
|
| 22 |
+
- eval_mean_iou: 0.7101
|
| 23 |
+
- eval_mean_accuracy: 0.8959
|
| 24 |
+
- eval_overall_accuracy: 0.9905
|
| 25 |
+
- eval_accuracy_unlabeled: nan
|
| 26 |
+
- eval_accuracy_healthy: 0.9792
|
| 27 |
+
- eval_accuracy_blast: 0.8497
|
| 28 |
+
- eval_accuracy_background: 0.9972
|
| 29 |
+
- eval_accuracy_alstonia herpes: 0.8615
|
| 30 |
+
- eval_accuracy_mango anthracnose: 0.8848
|
| 31 |
+
- eval_accuracy_pomengrate spots: 0.9391
|
| 32 |
+
- eval_accuracy_blight spots: 0.9297
|
| 33 |
+
- eval_accuracy_chlorisis: 0.9444
|
| 34 |
+
- eval_accuracy_leaf spots: 0.8855
|
| 35 |
+
- eval_accuracy_apple scab: 0.7057
|
| 36 |
+
- eval_accuracy_black rot: 0.8582
|
| 37 |
+
- eval_accuracy_cedar rust: 0.9160
|
| 38 |
+
- eval_iou_unlabeled: 0.0
|
| 39 |
+
- eval_iou_healthy: 0.9651
|
| 40 |
+
- eval_iou_blast: 0.7555
|
| 41 |
+
- eval_iou_background: 0.9950
|
| 42 |
+
- eval_iou_alstonia herpes: 0.7372
|
| 43 |
+
- eval_iou_mango anthracnose: 0.6383
|
| 44 |
+
- eval_iou_pomengrate spots: 0.8571
|
| 45 |
+
- eval_iou_blight spots: 0.5193
|
| 46 |
+
- eval_iou_chlorisis: 0.9064
|
| 47 |
+
- eval_iou_leaf spots: 0.6862
|
| 48 |
+
- eval_iou_apple scab: 0.6495
|
| 49 |
+
- eval_iou_black rot: 0.8001
|
| 50 |
+
- eval_iou_cedar rust: 0.7218
|
| 51 |
+
- eval_runtime: 151.4804
|
| 52 |
+
- eval_samples_per_second: 17.309
|
| 53 |
+
- eval_steps_per_second: 1.083
|
| 54 |
+
- epoch: 6.3310
|
| 55 |
+
- step: 8300
|
| 56 |
|
| 57 |
## Model description
|
| 58 |
|
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|
| 71 |
### Training hyperparameters
|
| 72 |
|
| 73 |
The following hyperparameters were used during training:
|
| 74 |
+
- learning_rate: 1e-05
|
| 75 |
+
- train_batch_size: 16
|
| 76 |
+
- eval_batch_size: 16
|
| 77 |
- seed: 42
|
| 78 |
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
| 79 |
- lr_scheduler_type: cosine
|
| 80 |
- lr_scheduler_warmup_steps: 200
|
| 81 |
+
- num_epochs: 20
|
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|
| 82 |
|
| 83 |
### Framework versions
|
| 84 |
|
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "nancyalarabawy/segformer-finetuned-segments-plantleafdisease-
|
| 3 |
"architectures": [
|
| 4 |
"SegformerForSemanticSegmentation"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "nancyalarabawy/segformer-finetuned-segments-plantleafdisease-DEC",
|
| 3 |
"architectures": [
|
| 4 |
"SegformerForSemanticSegmentation"
|
| 5 |
],
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 256130972
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:de4750359046f0ceeb76a16e197eaa8993e15fbbb8721606bcc9156741eec79c
|
| 3 |
size 256130972
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5368
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1b60e9ace122344aced7e608e86e49d89a963ac1152d3d3f615573cb031f7d9c
|
| 3 |
size 5368
|