Model save
Browse files- README.md +207 -207
- model.safetensors +1 -1
- runs/May21_18-34-47_5bff2b41c42c/events.out.tfevents.1716316488.5bff2b41c42c.34.0 +3 -0
- training_args.bin +1 -1
README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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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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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- Confusion Matrix: [[
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- Classification Report: precision recall f1-score support
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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accuracy 0.7645 569
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macro avg 0.7724 0.
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accuracy 0.7663 569
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macro avg 0.7817 0.7619 0.7561 569
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weighted avg 0.7810 0.7663 0.7578 569
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| 1.297 | 4.48 | 600 | 1.2208 | 0.7856 | [[37, 2, 0, 0, 4, 1, 1, 1, 3, 3], [2, 53, 0, 0, 1, 0, 1, 0, 2, 1], [0, 0, 32, 4, 1, 5, 0, 9, 0, 0], [2, 1, 0, 34, 1, 3, 0, 2, 0, 12], [1, 1, 1, 0, 51, 1, 0, 0, 0, 1], [0, 0, 4, 1, 0, 46, 0, 3, 0, 2], [1, 0, 1, 1, 3, 0, 53, 0, 2, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [5, 10, 0, 0, 0, 0, 10, 2, 33, 0], [0, 0, 1, 2, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
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0 0.7708 0.7115 0.7400 52
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1 0.7910 0.8833 0.8346 60
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2 0.7619 0.6275 0.6882 51
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3 0.7907 0.6182 0.6939 55
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4 0.8361 0.9107 0.8718 56
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5 0.8214 0.8214 0.8214 56
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6 0.8154 0.8413 0.8281 63
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8 0.8250 0.5500 0.6600 60
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9 0.7273 0.9333 0.8175 60
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accuracy 0.8049 569
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3 0.7826 0.6545 0.7129 55
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5 0.8333 0.8036 0.8182 56
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6 0.8413 0.8413 0.8413 63
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8 0.7907 0.5667 0.6602 60
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9 0.7500 0.9500 0.8382 60
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accuracy 0.8032 569
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macro avg 0.8031 0.8014 0.7981 569
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weighted avg 0.8040 0.8032 0.7993 569
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0 0.7358 0.7500 0.7429 52
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accuracy 0.8137 569
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.820738137082601
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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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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9688
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- Accuracy: 0.8207
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- Confusion Matrix: [[40, 3, 0, 0, 1, 2, 0, 1, 2, 3], [1, 57, 0, 0, 0, 0, 2, 0, 0, 0], [4, 0, 37, 1, 1, 4, 0, 4, 0, 0], [3, 1, 1, 36, 1, 2, 0, 0, 0, 11], [1, 1, 2, 0, 50, 0, 0, 0, 0, 2], [0, 0, 7, 1, 1, 46, 0, 0, 0, 1], [1, 0, 1, 2, 0, 0, 57, 0, 2, 0], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [5, 9, 0, 0, 0, 0, 10, 0, 36, 0], [0, 0, 0, 4, 0, 1, 0, 1, 0, 54]]
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- Classification Report: precision recall f1-score support
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0 0.7273 0.7692 0.7477 52
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1 0.8028 0.9500 0.8702 60
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2 0.7551 0.7255 0.7400 51
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3 0.8182 0.6545 0.7273 55
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4 0.9259 0.8929 0.9091 56
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5 0.8214 0.8214 0.8214 56
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6 0.8261 0.9048 0.8636 63
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7 0.9000 0.9643 0.9310 56
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8 0.9000 0.6000 0.7200 60
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9 0.7606 0.9000 0.8244 60
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accuracy 0.8207 569
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macro avg 0.8237 0.8183 0.8155 569
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weighted avg 0.8250 0.8207 0.8171 569
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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| 2.1705 | 0.75 | 100 | 2.1366 | 0.4376 | [[8, 7, 5, 0, 1, 4, 6, 11, 6, 4], [1, 55, 0, 0, 0, 0, 1, 0, 3, 0], [1, 3, 8, 0, 3, 9, 2, 23, 1, 1], [2, 5, 12, 5, 1, 10, 2, 5, 1, 12], [1, 7, 16, 0, 16, 2, 4, 2, 1, 7], [0, 2, 9, 0, 1, 30, 3, 4, 3, 4], [6, 5, 0, 0, 1, 0, 21, 3, 25, 2], [1, 1, 0, 0, 1, 0, 0, 53, 0, 0], [2, 17, 2, 0, 0, 0, 10, 2, 27, 0], [3, 4, 4, 2, 0, 8, 4, 8, 1, 26]] | precision recall f1-score support
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0 0.3200 0.1538 0.2078 52
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1 0.5189 0.9167 0.6627 60
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2 0.1429 0.1569 0.1495 51
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3 0.7143 0.0909 0.1613 55
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4 0.6667 0.2857 0.4000 56
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5 0.4762 0.5357 0.5042 56
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6 0.3962 0.3333 0.3621 63
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7 0.4775 0.9464 0.6347 56
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8 0.3971 0.4500 0.4219 60
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9 0.4643 0.4333 0.4483 60
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accuracy 0.4376 569
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macro avg 0.4574 0.4303 0.3952 569
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weighted avg 0.4600 0.4376 0.4012 569
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| 1.9462 | 1.49 | 200 | 1.9010 | 0.6573 | [[19, 5, 1, 0, 2, 2, 8, 9, 3, 3], [0, 56, 0, 0, 0, 0, 2, 0, 2, 0], [0, 2, 17, 0, 2, 7, 1, 21, 0, 1], [1, 2, 7, 14, 10, 3, 2, 2, 0, 14], [0, 1, 1, 0, 46, 0, 3, 1, 0, 4], [1, 0, 6, 0, 2, 42, 1, 3, 0, 1], [1, 2, 0, 1, 2, 0, 42, 0, 14, 1], [0, 0, 0, 0, 0, 0, 0, 56, 0, 0], [1, 12, 0, 0, 0, 0, 15, 2, 30, 0], [2, 0, 0, 0, 3, 0, 1, 2, 0, 52]] | precision recall f1-score support
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0 0.7600 0.3654 0.4935 52
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1 0.7000 0.9333 0.8000 60
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2 0.5312 0.3333 0.4096 51
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3 0.9333 0.2545 0.4000 55
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4 0.6866 0.8214 0.7480 56
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5 0.7778 0.7500 0.7636 56
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6 0.5600 0.6667 0.6087 63
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7 0.5833 1.0000 0.7368 56
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8 0.6122 0.5000 0.5505 60
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9 0.6842 0.8667 0.7647 60
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accuracy 0.6573 569
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macro avg 0.6829 0.6491 0.6275 569
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weighted avg 0.6813 0.6573 0.6322 569
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| 1.6891 | 2.24 | 300 | 1.6470 | 0.7469 | [[34, 3, 0, 0, 2, 2, 0, 4, 3, 4], [0, 56, 0, 0, 0, 0, 1, 0, 3, 0], [2, 1, 29, 0, 2, 5, 0, 11, 0, 1], [3, 1, 4, 22, 5, 2, 0, 0, 0, 18], [2, 1, 0, 0, 48, 1, 0, 0, 0, 4], [1, 0, 4, 0, 1, 44, 1, 2, 0, 3], [2, 1, 0, 3, 2, 0, 44, 0, 10, 1], [0, 0, 0, 0, 0, 1, 0, 55, 0, 0], [5, 10, 0, 0, 0, 0, 5, 3, 37, 0], [1, 0, 0, 2, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
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0 0.6800 0.6538 0.6667 52
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1 0.7671 0.9333 0.8421 60
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2 0.7838 0.5686 0.6591 51
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3 0.8148 0.4000 0.5366 55
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4 0.8000 0.8571 0.8276 56
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5 0.8000 0.7857 0.7928 56
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6 0.8627 0.6984 0.7719 63
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7 0.7237 0.9821 0.8333 56
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8 0.6981 0.6167 0.6549 60
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9 0.6437 0.9333 0.7619 60
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accuracy 0.7469 569
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macro avg 0.7574 0.7429 0.7347 569
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weighted avg 0.7578 0.7469 0.7370 569
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| 1.5299 | 2.99 | 400 | 1.4338 | 0.7645 | [[33, 4, 2, 0, 3, 2, 0, 2, 2, 4], [0, 57, 0, 0, 0, 0, 1, 0, 2, 0], [1, 1, 32, 1, 1, 6, 1, 7, 0, 1], [3, 1, 3, 24, 3, 2, 0, 1, 0, 18], [1, 1, 0, 0, 50, 1, 0, 0, 0, 3], [0, 0, 6, 0, 2, 44, 1, 1, 0, 2], [2, 2, 1, 2, 2, 0, 51, 0, 1, 2], [0, 0, 0, 0, 0, 2, 0, 54, 0, 0], [4, 11, 0, 0, 0, 0, 7, 3, 35, 0], [0, 0, 0, 4, 0, 0, 0, 1, 0, 55]] | precision recall f1-score support
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0 0.7500 0.6346 0.6875 52
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1 0.7403 0.9500 0.8321 60
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2 0.7273 0.6275 0.6737 51
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3 0.7742 0.4364 0.5581 55
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4 0.8197 0.8929 0.8547 56
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5 0.7719 0.7857 0.7788 56
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6 0.8361 0.8095 0.8226 63
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7 0.7826 0.9643 0.8640 56
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8 0.8750 0.5833 0.7000 60
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9 0.6471 0.9167 0.7586 60
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accuracy 0.7645 569
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macro avg 0.7724 0.7601 0.7530 569
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weighted avg 0.7734 0.7645 0.7556 569
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+
| 1.3327 | 3.73 | 500 | 1.3053 | 0.7698 | [[38, 3, 0, 0, 2, 2, 0, 1, 3, 3], [0, 57, 0, 0, 0, 0, 1, 0, 2, 0], [3, 1, 33, 2, 1, 3, 0, 7, 0, 1], [3, 1, 3, 22, 3, 2, 0, 1, 0, 20], [2, 1, 0, 0, 48, 1, 0, 0, 0, 4], [1, 0, 6, 1, 1, 42, 0, 2, 0, 3], [4, 0, 0, 1, 1, 0, 54, 0, 1, 2], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [7, 9, 0, 0, 0, 0, 9, 1, 34, 0], [0, 0, 0, 3, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
|
154 |
|
155 |
+
0 0.6552 0.7308 0.6909 52
|
156 |
+
1 0.7917 0.9500 0.8636 60
|
157 |
+
2 0.7674 0.6471 0.7021 51
|
158 |
+
3 0.7586 0.4000 0.5238 55
|
159 |
+
4 0.8571 0.8571 0.8571 56
|
160 |
+
5 0.8235 0.7500 0.7850 56
|
161 |
+
6 0.8438 0.8571 0.8504 63
|
162 |
+
7 0.8060 0.9643 0.8780 56
|
163 |
+
8 0.8500 0.5667 0.6800 60
|
164 |
+
9 0.6292 0.9333 0.7517 60
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
165 |
|
166 |
+
accuracy 0.7698 569
|
167 |
+
macro avg 0.7783 0.7656 0.7583 569
|
168 |
+
weighted avg 0.7796 0.7698 0.7609 569
|
169 |
|
|
170 |
+
| 1.2796 | 4.48 | 600 | 1.2046 | 0.7909 | [[38, 3, 0, 0, 1, 2, 1, 1, 3, 3], [1, 56, 0, 0, 0, 0, 1, 0, 2, 0], [3, 1, 29, 2, 1, 6, 0, 9, 0, 0], [2, 1, 1, 35, 2, 2, 0, 1, 0, 11], [1, 1, 0, 0, 50, 1, 0, 0, 0, 3], [0, 0, 5, 0, 1, 45, 1, 2, 0, 2], [2, 1, 1, 2, 1, 0, 54, 0, 1, 1], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [5, 9, 0, 0, 0, 0, 9, 3, 34, 0], [0, 0, 0, 4, 0, 0, 0, 1, 0, 55]] | precision recall f1-score support
|
171 |
|
172 |
+
0 0.7308 0.7308 0.7308 52
|
173 |
+
1 0.7778 0.9333 0.8485 60
|
174 |
+
2 0.7838 0.5686 0.6591 51
|
175 |
+
3 0.8140 0.6364 0.7143 55
|
176 |
+
4 0.8929 0.8929 0.8929 56
|
177 |
+
5 0.7895 0.8036 0.7965 56
|
178 |
+
6 0.8182 0.8571 0.8372 63
|
179 |
+
7 0.7606 0.9643 0.8504 56
|
180 |
+
8 0.8500 0.5667 0.6800 60
|
181 |
+
9 0.7333 0.9167 0.8148 60
|
182 |
+
|
183 |
+
accuracy 0.7909 569
|
184 |
+
macro avg 0.7951 0.7870 0.7824 569
|
185 |
+
weighted avg 0.7957 0.7909 0.7846 569
|
186 |
|
|
187 |
+
| 1.185 | 5.22 | 700 | 1.1314 | 0.8067 | [[38, 3, 0, 0, 3, 1, 0, 1, 3, 3], [1, 57, 0, 0, 0, 0, 1, 0, 1, 0], [2, 1, 35, 3, 1, 5, 0, 4, 0, 0], [3, 1, 1, 32, 2, 2, 0, 0, 0, 14], [1, 1, 0, 0, 50, 1, 0, 0, 0, 3], [0, 0, 7, 1, 1, 45, 0, 0, 0, 2], [2, 0, 1, 1, 1, 0, 56, 0, 1, 1], [0, 0, 1, 1, 0, 1, 0, 53, 0, 0], [5, 9, 0, 0, 0, 0, 9, 0, 37, 0], [0, 0, 0, 3, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
|
188 |
|
189 |
+
0 0.7308 0.7308 0.7308 52
|
190 |
+
1 0.7917 0.9500 0.8636 60
|
191 |
+
2 0.7778 0.6863 0.7292 51
|
192 |
+
3 0.7805 0.5818 0.6667 55
|
193 |
+
4 0.8621 0.8929 0.8772 56
|
194 |
+
5 0.8182 0.8036 0.8108 56
|
195 |
+
6 0.8485 0.8889 0.8682 63
|
196 |
+
7 0.8983 0.9464 0.9217 56
|
197 |
+
8 0.8810 0.6167 0.7255 60
|
198 |
+
9 0.7089 0.9333 0.8058 60
|
199 |
+
|
200 |
+
accuracy 0.8067 569
|
201 |
+
macro avg 0.8098 0.8031 0.7999 569
|
202 |
+
weighted avg 0.8108 0.8067 0.8021 569
|
203 |
|
|
204 |
+
| 1.1362 | 5.97 | 800 | 1.0808 | 0.8049 | [[39, 4, 0, 0, 1, 2, 0, 1, 2, 3], [1, 57, 0, 0, 0, 0, 1, 0, 1, 0], [4, 1, 35, 1, 1, 5, 0, 4, 0, 0], [2, 1, 1, 32, 2, 2, 0, 1, 0, 14], [1, 1, 0, 0, 50, 1, 0, 0, 0, 3], [0, 0, 6, 1, 1, 46, 0, 0, 0, 2], [2, 1, 1, 2, 1, 0, 54, 0, 1, 1], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [5, 10, 0, 0, 0, 0, 9, 1, 35, 0], [0, 0, 0, 3, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
|
205 |
|
206 |
+
0 0.7222 0.7500 0.7358 52
|
207 |
+
1 0.7600 0.9500 0.8444 60
|
208 |
+
2 0.7955 0.6863 0.7368 51
|
209 |
+
3 0.8205 0.5818 0.6809 55
|
210 |
4 0.8929 0.8929 0.8929 56
|
211 |
+
5 0.8070 0.8214 0.8142 56
|
212 |
+
6 0.8438 0.8571 0.8504 63
|
213 |
+
7 0.8710 0.9643 0.9153 56
|
214 |
+
8 0.8974 0.5833 0.7071 60
|
215 |
+
9 0.7089 0.9333 0.8058 60
|
216 |
|
217 |
accuracy 0.8049 569
|
218 |
+
macro avg 0.8119 0.8020 0.7983 569
|
219 |
+
weighted avg 0.8126 0.8049 0.7999 569
|
220 |
|
|
221 |
+
| 1.0907 | 6.72 | 900 | 1.0424 | 0.8155 | [[40, 3, 0, 0, 1, 2, 0, 1, 2, 3], [1, 57, 0, 0, 0, 0, 1, 0, 1, 0], [4, 0, 37, 1, 1, 4, 0, 4, 0, 0], [2, 1, 0, 33, 2, 3, 0, 1, 0, 13], [1, 1, 2, 0, 49, 0, 0, 0, 0, 3], [0, 0, 7, 1, 1, 46, 0, 0, 0, 1], [2, 0, 1, 1, 0, 0, 57, 0, 1, 1], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [6, 9, 0, 0, 0, 0, 9, 1, 35, 0], [0, 0, 0, 2, 0, 1, 0, 1, 0, 56]] | precision recall f1-score support
|
222 |
+
|
223 |
+
0 0.7143 0.7692 0.7407 52
|
224 |
+
1 0.8028 0.9500 0.8702 60
|
225 |
+
2 0.7708 0.7255 0.7475 51
|
226 |
+
3 0.8684 0.6000 0.7097 55
|
227 |
+
4 0.9074 0.8750 0.8909 56
|
228 |
+
5 0.8070 0.8214 0.8142 56
|
229 |
+
6 0.8507 0.9048 0.8769 63
|
230 |
+
7 0.8710 0.9643 0.9153 56
|
231 |
+
8 0.8974 0.5833 0.7071 60
|
232 |
+
9 0.7273 0.9333 0.8175 60
|
233 |
|
234 |
+
accuracy 0.8155 569
|
235 |
+
macro avg 0.8217 0.8127 0.8090 569
|
236 |
+
weighted avg 0.8229 0.8155 0.8108 569
|
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|
|
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|
|
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|
|
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|
237 |
|
|
238 |
+
| 1.0281 | 7.46 | 1000 | 1.0109 | 0.8137 | [[38, 3, 0, 0, 1, 2, 1, 1, 3, 3], [1, 57, 0, 0, 0, 0, 1, 0, 1, 0], [4, 0, 37, 1, 1, 4, 0, 4, 0, 0], [2, 1, 0, 35, 2, 3, 0, 1, 0, 11], [1, 1, 2, 0, 49, 0, 0, 0, 0, 3], [0, 0, 7, 1, 1, 46, 0, 0, 0, 1], [1, 0, 1, 2, 0, 0, 57, 0, 2, 0], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [6, 8, 0, 0, 0, 0, 9, 1, 36, 0], [0, 0, 0, 5, 0, 0, 0, 1, 0, 54]] | precision recall f1-score support
|
239 |
|
240 |
+
0 0.7170 0.7308 0.7238 52
|
241 |
+
1 0.8143 0.9500 0.8769 60
|
242 |
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2 0.7708 0.7255 0.7475 51
|
243 |
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3 0.7955 0.6364 0.7071 55
|
244 |
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4 0.9074 0.8750 0.8909 56
|
245 |
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5 0.8214 0.8214 0.8214 56
|
246 |
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6 0.8382 0.9048 0.8702 63
|
247 |
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7 0.8710 0.9643 0.9153 56
|
248 |
+
8 0.8571 0.6000 0.7059 60
|
249 |
+
9 0.7500 0.9000 0.8182 60
|
250 |
+
|
251 |
+
accuracy 0.8137 569
|
252 |
+
macro avg 0.8143 0.8108 0.8077 569
|
253 |
+
weighted avg 0.8155 0.8137 0.8096 569
|
254 |
|
|
255 |
+
| 0.9576 | 8.21 | 1100 | 0.9912 | 0.8155 | [[39, 3, 0, 0, 1, 2, 0, 1, 3, 3], [1, 57, 0, 0, 0, 0, 1, 0, 1, 0], [3, 0, 37, 2, 1, 4, 0, 4, 0, 0], [2, 1, 1, 35, 1, 2, 0, 1, 0, 12], [1, 1, 2, 0, 49, 0, 0, 0, 0, 3], [0, 0, 7, 1, 1, 46, 0, 0, 0, 1], [1, 0, 1, 2, 0, 0, 56, 0, 3, 0], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [6, 10, 0, 0, 0, 0, 7, 1, 36, 0], [0, 0, 0, 4, 0, 0, 0, 1, 0, 55]] | precision recall f1-score support
|
256 |
|
257 |
0 0.7358 0.7500 0.7429 52
|
258 |
+
1 0.7917 0.9500 0.8636 60
|
259 |
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2 0.7551 0.7255 0.7400 51
|
260 |
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3 0.7955 0.6364 0.7071 55
|
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|
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5 0.8364 0.8214 0.8288 56
|
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|
264 |
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7 0.8710 0.9643 0.9153 56
|
265 |
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8 0.8372 0.6000 0.6990 60
|
266 |
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9 0.7432 0.9167 0.8209 60
|
267 |
+
|
268 |
+
accuracy 0.8155 569
|
269 |
+
macro avg 0.8165 0.8128 0.8099 569
|
270 |
+
weighted avg 0.8179 0.8155 0.8117 569
|
271 |
+
|
|
272 |
+
| 1.0678 | 8.96 | 1200 | 0.9745 | 0.8137 | [[37, 3, 0, 0, 1, 2, 2, 1, 3, 3], [1, 57, 0, 0, 0, 0, 2, 0, 0, 0], [4, 0, 36, 1, 1, 5, 0, 4, 0, 0], [3, 1, 0, 37, 0, 3, 0, 0, 0, 11], [1, 1, 2, 0, 49, 0, 0, 0, 0, 3], [0, 0, 7, 0, 1, 46, 1, 0, 0, 1], [1, 0, 1, 2, 0, 0, 57, 0, 2, 0], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [5, 8, 0, 0, 0, 0, 11, 0, 36, 0], [0, 0, 0, 4, 0, 1, 0, 1, 0, 54]] | precision recall f1-score support
|
273 |
+
|
274 |
+
0 0.7115 0.7115 0.7115 52
|
275 |
+
1 0.8143 0.9500 0.8769 60
|
276 |
+
2 0.7660 0.7059 0.7347 51
|
277 |
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3 0.8409 0.6727 0.7475 55
|
278 |
+
4 0.9423 0.8750 0.9074 56
|
279 |
+
5 0.7931 0.8214 0.8070 56
|
280 |
+
6 0.7808 0.9048 0.8382 63
|
281 |
+
7 0.9000 0.9643 0.9310 56
|
282 |
+
8 0.8780 0.6000 0.7129 60
|
283 |
+
9 0.7500 0.9000 0.8182 60
|
284 |
|
285 |
accuracy 0.8137 569
|
286 |
+
macro avg 0.8177 0.8106 0.8085 569
|
287 |
+
weighted avg 0.8183 0.8137 0.8102 569
|
288 |
|
|
289 |
+
| 1.0138 | 9.7 | 1300 | 0.9688 | 0.8207 | [[40, 3, 0, 0, 1, 2, 0, 1, 2, 3], [1, 57, 0, 0, 0, 0, 2, 0, 0, 0], [4, 0, 37, 1, 1, 4, 0, 4, 0, 0], [3, 1, 1, 36, 1, 2, 0, 0, 0, 11], [1, 1, 2, 0, 50, 0, 0, 0, 0, 2], [0, 0, 7, 1, 1, 46, 0, 0, 0, 1], [1, 0, 1, 2, 0, 0, 57, 0, 2, 0], [0, 0, 1, 0, 0, 1, 0, 54, 0, 0], [5, 9, 0, 0, 0, 0, 10, 0, 36, 0], [0, 0, 0, 4, 0, 1, 0, 1, 0, 54]] | precision recall f1-score support
|
290 |
|
291 |
+
0 0.7273 0.7692 0.7477 52
|
292 |
+
1 0.8028 0.9500 0.8702 60
|
293 |
+
2 0.7551 0.7255 0.7400 51
|
294 |
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3 0.8182 0.6545 0.7273 55
|
295 |
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4 0.9259 0.8929 0.9091 56
|
296 |
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5 0.8214 0.8214 0.8214 56
|
297 |
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6 0.8261 0.9048 0.8636 63
|
298 |
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7 0.9000 0.9643 0.9310 56
|
299 |
+
8 0.9000 0.6000 0.7200 60
|
300 |
+
9 0.7606 0.9000 0.8244 60
|
301 |
+
|
302 |
+
accuracy 0.8207 569
|
303 |
+
macro avg 0.8237 0.8183 0.8155 569
|
304 |
+
weighted avg 0.8250 0.8207 0.8171 569
|
305 |
|
|
306 |
|
307 |
|
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CHANGED
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