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Raihan004
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Action_agent

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vit
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  1. README.md +207 -207
  2. model.safetensors +1 -1
  3. runs/May21_18-34-47_5bff2b41c42c/events.out.tfevents.1716316488.5bff2b41c42c.34.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.81195079086116
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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
@@ -32,25 +32,25 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.9874
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- - Accuracy: 0.8120
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- - Confusion Matrix: [[39, 3, 0, 0, 3, 1, 0, 1, 2, 3], [2, 55, 0, 0, 0, 0, 1, 0, 1, 1], [0, 0, 40, 4, 1, 3, 0, 3, 0, 0], [3, 0, 0, 36, 0, 3, 0, 1, 0, 12], [1, 2, 1, 0, 50, 1, 0, 0, 0, 1], [0, 0, 8, 1, 0, 46, 0, 0, 0, 1], [1, 0, 0, 3, 1, 1, 54, 0, 3, 0], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [6, 10, 0, 0, 0, 0, 9, 1, 34, 0], [0, 0, 1, 2, 0, 0, 0, 1, 0, 56]]
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  - Classification Report: precision recall f1-score support
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- 0 0.7500 0.7500 0.7500 52
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- 1 0.7857 0.9167 0.8462 60
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- 2 0.7547 0.7843 0.7692 51
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- 3 0.7660 0.6545 0.7059 55
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- 4 0.9091 0.8929 0.9009 56
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- 5 0.8364 0.8214 0.8288 56
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- 6 0.8438 0.8571 0.8504 63
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- 7 0.8814 0.9286 0.9043 56
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- 8 0.8500 0.5667 0.6800 60
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- 9 0.7568 0.9333 0.8358 60
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- accuracy 0.8120 569
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- macro avg 0.8134 0.8106 0.8072 569
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- weighted avg 0.8145 0.8120 0.8082 569
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  ## Model description
@@ -82,226 +82,226 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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- | 2.1815 | 0.75 | 100 | 2.1369 | 0.4605 | [[0, 11, 3, 2, 10, 3, 4, 11, 5, 3], [0, 52, 0, 0, 1, 0, 2, 1, 4, 0], [1, 6, 6, 1, 6, 14, 3, 6, 4, 4], [2, 7, 5, 14, 6, 7, 1, 8, 2, 3], [2, 3, 2, 5, 30, 2, 8, 2, 2, 0], [1, 5, 5, 1, 2, 34, 3, 5, 0, 0], [0, 1, 0, 1, 7, 1, 25, 1, 27, 0], [0, 4, 0, 0, 0, 1, 1, 50, 0, 0], [1, 15, 0, 0, 6, 0, 7, 2, 29, 0], [2, 3, 3, 10, 10, 1, 3, 5, 1, 22]] | precision recall f1-score support
86
-
87
- 0 0.0000 0.0000 0.0000 52
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- 1 0.4860 0.8667 0.6228 60
89
- 2 0.2500 0.1176 0.1600 51
90
- 3 0.4118 0.2545 0.3146 55
91
- 4 0.3846 0.5357 0.4478 56
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- 5 0.5397 0.6071 0.5714 56
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- 6 0.4386 0.3968 0.4167 63
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- 7 0.5495 0.8929 0.6803 56
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- 8 0.3919 0.4833 0.4328 60
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- 9 0.6875 0.3667 0.4783 60
97
-
98
- accuracy 0.4605 569
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- macro avg 0.4139 0.4521 0.4125 569
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- weighted avg 0.4209 0.4605 0.4199 569
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  |
102
- | 1.9348 | 1.49 | 200 | 1.8936 | 0.6538 | [[6, 8, 3, 1, 11, 1, 4, 13, 2, 3], [1, 56, 0, 0, 0, 0, 1, 0, 1, 1], [0, 1, 27, 0, 7, 1, 0, 15, 0, 0], [0, 3, 4, 27, 10, 1, 0, 4, 0, 6], [1, 1, 3, 0, 47, 0, 1, 1, 0, 2], [0, 0, 14, 0, 3, 32, 1, 5, 0, 1], [0, 1, 0, 2, 5, 0, 46, 0, 8, 1], [0, 1, 0, 0, 0, 1, 1, 53, 0, 0], [0, 14, 0, 0, 2, 0, 11, 4, 29, 0], [0, 0, 0, 5, 4, 1, 0, 1, 0, 49]] | precision recall f1-score support
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-
104
- 0 0.7500 0.1154 0.2000 52
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- 1 0.6588 0.9333 0.7724 60
106
- 2 0.5294 0.5294 0.5294 51
107
- 3 0.7714 0.4909 0.6000 55
108
- 4 0.5281 0.8393 0.6483 56
109
- 5 0.8649 0.5714 0.6882 56
110
- 6 0.7077 0.7302 0.7188 63
111
- 7 0.5521 0.9464 0.6974 56
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- 8 0.7250 0.4833 0.5800 60
113
- 9 0.7778 0.8167 0.7967 60
114
-
115
- accuracy 0.6538 569
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- macro avg 0.6865 0.6456 0.6231 569
117
- weighted avg 0.6883 0.6538 0.6301 569
118
  |
119
- | 1.6938 | 2.24 | 300 | 1.6475 | 0.7487 | [[26, 6, 2, 2, 5, 1, 2, 2, 3, 3], [1, 55, 0, 0, 0, 0, 1, 0, 2, 1], [0, 1, 28, 3, 5, 2, 1, 11, 0, 0], [0, 2, 1, 32, 5, 2, 0, 3, 0, 10], [0, 1, 1, 1, 48, 1, 0, 0, 0, 4], [0, 0, 3, 1, 1, 45, 1, 3, 0, 2], [2, 1, 0, 1, 3, 0, 47, 0, 8, 1], [0, 1, 0, 0, 0, 1, 1, 53, 0, 0], [1, 10, 0, 0, 1, 0, 8, 3, 37, 0], [0, 0, 0, 3, 1, 0, 0, 1, 0, 55]] | precision recall f1-score support
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-
121
- 0 0.8667 0.5000 0.6341 52
122
- 1 0.7143 0.9167 0.8029 60
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- 2 0.8000 0.5490 0.6512 51
124
- 3 0.7442 0.5818 0.6531 55
125
- 4 0.6957 0.8571 0.7680 56
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- 5 0.8654 0.8036 0.8333 56
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- 6 0.7705 0.7460 0.7581 63
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- 7 0.6974 0.9464 0.8030 56
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- 8 0.7400 0.6167 0.6727 60
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- 9 0.7237 0.9167 0.8088 60
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-
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- accuracy 0.7487 569
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- macro avg 0.7618 0.7434 0.7385 569
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- weighted avg 0.7601 0.7487 0.7409 569
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  |
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- | 1.537 | 2.99 | 400 | 1.4478 | 0.7645 | [[35, 3, 0, 0, 4, 1, 1, 1, 3, 4], [1, 55, 0, 0, 1, 0, 1, 0, 1, 1], [1, 0, 32, 4, 1, 3, 0, 10, 0, 0], [1, 2, 1, 27, 5, 3, 0, 1, 0, 15], [1, 1, 0, 0, 50, 1, 0, 0, 0, 3], [0, 0, 7, 0, 1, 42, 0, 3, 0, 3], [1, 0, 1, 1, 3, 0, 52, 0, 4, 1], [0, 0, 2, 0, 0, 1, 1, 52, 0, 0], [4, 11, 0, 0, 0, 0, 10, 2, 33, 0], [0, 0, 1, 1, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
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138
- 0 0.7955 0.6731 0.7292 52
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- 1 0.7639 0.9167 0.8333 60
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  2 0.7273 0.6275 0.6737 51
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- 3 0.8182 0.4909 0.6136 55
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- 4 0.7692 0.8929 0.8264 56
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- 5 0.8235 0.7500 0.7850 56
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- 6 0.8000 0.8254 0.8125 63
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- 7 0.7429 0.9286 0.8254 56
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- 8 0.8049 0.5500 0.6535 60
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- 9 0.6786 0.9500 0.7917 60
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149
  accuracy 0.7645 569
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- macro avg 0.7724 0.7605 0.7544 569
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- weighted avg 0.7724 0.7645 0.7564 569
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  |
153
- | 1.3465 | 3.73 | 500 | 1.3222 | 0.7663 | [[35, 4, 0, 0, 3, 1, 1, 1, 2, 5], [1, 56, 0, 0, 0, 0, 1, 0, 1, 1], [1, 1, 31, 5, 1, 2, 0, 10, 0, 0], [3, 1, 0, 26, 1, 3, 0, 1, 0, 20], [1, 1, 0, 0, 50, 1, 0, 0, 0, 3], [0, 1, 4, 0, 0, 45, 0, 3, 0, 3], [2, 0, 0, 1, 3, 0, 53, 0, 2, 2], [0, 0, 2, 1, 0, 1, 1, 51, 0, 0], [4, 11, 0, 0, 0, 0, 11, 3, 31, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 58]] | precision recall f1-score support
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155
- 0 0.7447 0.6731 0.7071 52
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- 1 0.7467 0.9333 0.8296 60
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- 2 0.8158 0.6078 0.6966 51
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- 3 0.7879 0.4727 0.5909 55
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- 4 0.8621 0.8929 0.8772 56
160
- 5 0.8491 0.8036 0.8257 56
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- 6 0.7910 0.8413 0.8154 63
162
- 7 0.7286 0.9107 0.8095 56
163
- 8 0.8611 0.5167 0.6458 60
164
- 9 0.6304 0.9667 0.7632 60
165
-
166
- 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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- |
170
- | 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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-
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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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- 7 0.7429 0.9286 0.8254 56
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- 8 0.8250 0.5500 0.6600 60
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- 9 0.7273 0.9333 0.8175 60
182
 
183
- accuracy 0.7856 569
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- macro avg 0.7882 0.7826 0.7781 569
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- weighted avg 0.7888 0.7856 0.7798 569
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  |
187
- | 1.2028 | 5.22 | 700 | 1.1493 | 0.7926 | [[37, 4, 0, 0, 3, 1, 0, 1, 2, 4], [2, 54, 0, 0, 1, 0, 1, 0, 1, 1], [0, 0, 38, 4, 1, 4, 0, 4, 0, 0], [2, 1, 0, 29, 1, 3, 0, 1, 0, 18], [1, 1, 0, 0, 52, 1, 0, 0, 0, 1], [0, 0, 6, 1, 0, 46, 0, 1, 0, 2], [1, 0, 1, 1, 2, 0, 54, 0, 2, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [5, 12, 0, 0, 0, 0, 10, 1, 32, 0], [0, 0, 0, 2, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
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189
- 0 0.7708 0.7115 0.7400 52
190
- 1 0.7500 0.9000 0.8182 60
191
- 2 0.7917 0.7451 0.7677 51
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- 3 0.7632 0.5273 0.6237 55
193
- 4 0.8667 0.9286 0.8966 56
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- 5 0.8364 0.8214 0.8288 56
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- 6 0.8308 0.8571 0.8438 63
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- 7 0.8525 0.9286 0.8889 56
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- 8 0.8649 0.5333 0.6598 60
198
- 9 0.6706 0.9500 0.7862 60
199
-
200
- accuracy 0.7926 569
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- macro avg 0.7997 0.7903 0.7854 569
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- weighted avg 0.7997 0.7926 0.7862 569
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  |
204
- | 1.1565 | 5.97 | 800 | 1.1004 | 0.7944 | [[37, 4, 0, 0, 3, 1, 0, 1, 3, 3], [1, 55, 0, 0, 1, 0, 1, 0, 1, 1], [1, 1, 37, 3, 1, 4, 0, 4, 0, 0], [2, 1, 0, 34, 0, 3, 0, 1, 0, 14], [1, 2, 1, 0, 50, 1, 0, 0, 0, 1], [0, 0, 9, 1, 0, 44, 0, 0, 0, 2], [1, 0, 1, 1, 1, 0, 52, 0, 5, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [5, 12, 0, 0, 0, 0, 9, 0, 34, 0], [0, 0, 0, 2, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
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206
- 0 0.7708 0.7115 0.7400 52
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- 1 0.7333 0.9167 0.8148 60
208
- 2 0.7255 0.7255 0.7255 51
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- 3 0.8095 0.6182 0.7010 55
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- 4 0.8929 0.8929 0.8929 56
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- 5 0.8302 0.7857 0.8073 56
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- 6 0.8387 0.8254 0.8320 63
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- 7 0.8814 0.9286 0.9043 56
214
- 8 0.7907 0.5667 0.6602 60
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- 9 0.7125 0.9500 0.8143 60
216
-
217
- accuracy 0.7944 569
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- macro avg 0.7985 0.7921 0.7892 569
219
- weighted avg 0.7987 0.7944 0.7903 569
220
  |
221
- | 1.1101 | 6.72 | 900 | 1.0604 | 0.8049 | [[39, 3, 0, 0, 3, 1, 0, 1, 2, 3], [2, 54, 0, 0, 1, 0, 1, 0, 1, 1], [1, 0, 40, 3, 1, 3, 0, 3, 0, 0], [3, 0, 0, 35, 0, 3, 0, 1, 0, 13], [1, 1, 1, 1, 50, 1, 0, 0, 0, 1], [0, 0, 8, 1, 0, 45, 0, 0, 0, 2], [1, 0, 2, 1, 1, 0, 53, 0, 3, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [5, 10, 0, 0, 0, 0, 10, 2, 33, 0], [0, 0, 1, 1, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
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223
- 0 0.7500 0.7500 0.7500 52
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- 1 0.7941 0.9000 0.8438 60
225
- 2 0.7273 0.7843 0.7547 51
226
- 3 0.8140 0.6364 0.7143 55
227
  4 0.8929 0.8929 0.8929 56
228
- 5 0.8491 0.8036 0.8257 56
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- 6 0.8281 0.8413 0.8346 63
230
- 7 0.8667 0.9286 0.8966 56
231
- 8 0.8462 0.5500 0.6667 60
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- 9 0.7215 0.9500 0.8201 60
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234
  accuracy 0.8049 569
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- macro avg 0.8090 0.8037 0.7999 569
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- weighted avg 0.8099 0.8049 0.8008 569
237
  |
238
- | 1.0418 | 7.46 | 1000 | 1.0281 | 0.8032 | [[37, 3, 0, 0, 3, 1, 0, 1, 4, 3], [2, 54, 0, 0, 1, 0, 1, 0, 1, 1], [0, 0, 39, 4, 1, 4, 0, 3, 0, 0], [3, 0, 0, 36, 0, 3, 0, 1, 0, 12], [1, 2, 1, 0, 50, 1, 0, 0, 0, 1], [0, 0, 8, 1, 0, 45, 0, 0, 0, 2], [1, 0, 1, 3, 1, 0, 53, 0, 4, 0], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [6, 10, 0, 0, 0, 0, 9, 1, 34, 0], [0, 0, 1, 1, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
 
 
 
 
 
 
 
 
 
 
 
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240
- 0 0.7400 0.7115 0.7255 52
241
- 1 0.7826 0.9000 0.8372 60
242
- 2 0.7358 0.7647 0.7500 51
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- 3 0.7826 0.6545 0.7129 55
244
- 4 0.8929 0.8929 0.8929 56
245
- 5 0.8333 0.8036 0.8182 56
246
- 6 0.8413 0.8413 0.8413 63
247
- 7 0.8814 0.9286 0.9043 56
248
- 8 0.7907 0.5667 0.6602 60
249
- 9 0.7500 0.9500 0.8382 60
250
-
251
- accuracy 0.8032 569
252
- macro avg 0.8031 0.8014 0.7981 569
253
- weighted avg 0.8040 0.8032 0.7993 569
254
  |
255
- | 0.9723 | 8.21 | 1100 | 1.0077 | 0.8084 | [[38, 3, 0, 0, 3, 1, 0, 1, 3, 3], [2, 55, 0, 0, 0, 0, 1, 0, 1, 1], [0, 0, 41, 4, 1, 2, 0, 3, 0, 0], [3, 0, 0, 36, 0, 3, 0, 1, 0, 12], [1, 2, 1, 0, 50, 1, 0, 0, 0, 1], [0, 0, 9, 1, 0, 44, 0, 0, 0, 2], [1, 0, 1, 1, 1, 0, 53, 0, 4, 2], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [6, 10, 0, 0, 0, 0, 9, 1, 34, 0], [0, 0, 1, 1, 0, 0, 0, 1, 0, 57]] | precision recall f1-score support
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257
- 0 0.7451 0.7308 0.7379 52
258
- 1 0.7857 0.9167 0.8462 60
259
- 2 0.7321 0.8039 0.7664 51
260
- 3 0.8182 0.6545 0.7273 55
261
- 4 0.9091 0.8929 0.9009 56
262
- 5 0.8627 0.7857 0.8224 56
263
- 6 0.8413 0.8413 0.8413 63
264
- 7 0.8814 0.9286 0.9043 56
265
- 8 0.8095 0.5667 0.6667 60
266
- 9 0.7308 0.9500 0.8261 60
267
-
268
- accuracy 0.8084 569
269
- macro avg 0.8116 0.8071 0.8039 569
270
- weighted avg 0.8123 0.8084 0.8048 569
271
  |
272
- | 1.0853 | 8.96 | 1200 | 0.9924 | 0.8137 | [[39, 3, 0, 0, 3, 1, 0, 1, 2, 3], [2, 55, 0, 0, 0, 0, 1, 0, 1, 1], [1, 0, 39, 3, 1, 4, 0, 3, 0, 0], [3, 0, 0, 36, 0, 3, 0, 1, 0, 12], [1, 2, 1, 0, 50, 1, 0, 0, 0, 1], [0, 0, 7, 1, 0, 47, 0, 0, 0, 1], [1, 0, 0, 2, 1, 1, 55, 0, 3, 0], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [6, 10, 0, 0, 0, 0, 9, 1, 34, 0], [0, 0, 1, 2, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
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274
  0 0.7358 0.7500 0.7429 52
275
- 1 0.7857 0.9167 0.8462 60
276
- 2 0.7647 0.7647 0.7647 51
277
- 3 0.8000 0.6545 0.7200 55
278
- 4 0.9091 0.8929 0.9009 56
279
- 5 0.8246 0.8393 0.8319 56
280
- 6 0.8462 0.8730 0.8594 63
281
- 7 0.8814 0.9286 0.9043 56
282
- 8 0.8500 0.5667 0.6800 60
283
- 9 0.7568 0.9333 0.8358 60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
284
 
285
  accuracy 0.8137 569
286
- macro avg 0.8154 0.8120 0.8086 569
287
- weighted avg 0.8165 0.8137 0.8098 569
288
  |
289
- | 1.0291 | 9.7 | 1300 | 0.9874 | 0.8120 | [[39, 3, 0, 0, 3, 1, 0, 1, 2, 3], [2, 55, 0, 0, 0, 0, 1, 0, 1, 1], [0, 0, 40, 4, 1, 3, 0, 3, 0, 0], [3, 0, 0, 36, 0, 3, 0, 1, 0, 12], [1, 2, 1, 0, 50, 1, 0, 0, 0, 1], [0, 0, 8, 1, 0, 46, 0, 0, 0, 1], [1, 0, 0, 3, 1, 1, 54, 0, 3, 0], [0, 0, 3, 1, 0, 0, 0, 52, 0, 0], [6, 10, 0, 0, 0, 0, 9, 1, 34, 0], [0, 0, 1, 2, 0, 0, 0, 1, 0, 56]] | precision recall f1-score support
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291
- 0 0.7500 0.7500 0.7500 52
292
- 1 0.7857 0.9167 0.8462 60
293
- 2 0.7547 0.7843 0.7692 51
294
- 3 0.7660 0.6545 0.7059 55
295
- 4 0.9091 0.8929 0.9009 56
296
- 5 0.8364 0.8214 0.8288 56
297
- 6 0.8438 0.8571 0.8504 63
298
- 7 0.8814 0.9286 0.9043 56
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- 8 0.8500 0.5667 0.6800 60
300
- 9 0.7568 0.9333 0.8358 60
301
-
302
- accuracy 0.8120 569
303
- macro avg 0.8134 0.8106 0.8072 569
304
- weighted avg 0.8145 0.8120 0.8082 569
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  |
306
 
307
 
 
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.820738137082601
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  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.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 0.9688
36
+ - Accuracy: 0.8207
37
+ - 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]]
38
  - Classification Report: precision recall f1-score support
39
 
40
+ 0 0.7273 0.7692 0.7477 52
41
+ 1 0.8028 0.9500 0.8702 60
42
+ 2 0.7551 0.7255 0.7400 51
43
+ 3 0.8182 0.6545 0.7273 55
44
+ 4 0.9259 0.8929 0.9091 56
45
+ 5 0.8214 0.8214 0.8214 56
46
+ 6 0.8261 0.9048 0.8636 63
47
+ 7 0.9000 0.9643 0.9310 56
48
+ 8 0.9000 0.6000 0.7200 60
49
+ 9 0.7606 0.9000 0.8244 60
50
 
51
+ accuracy 0.8207 569
52
+ macro avg 0.8237 0.8183 0.8155 569
53
+ weighted avg 0.8250 0.8207 0.8171 569
54
 
55
 
56
  ## Model description
 
82
 
83
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
84
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
85
+ | 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
86
+
87
+ 0 0.3200 0.1538 0.2078 52
88
+ 1 0.5189 0.9167 0.6627 60
89
+ 2 0.1429 0.1569 0.1495 51
90
+ 3 0.7143 0.0909 0.1613 55
91
+ 4 0.6667 0.2857 0.4000 56
92
+ 5 0.4762 0.5357 0.5042 56
93
+ 6 0.3962 0.3333 0.3621 63
94
+ 7 0.4775 0.9464 0.6347 56
95
+ 8 0.3971 0.4500 0.4219 60
96
+ 9 0.4643 0.4333 0.4483 60
97
+
98
+ accuracy 0.4376 569
99
+ macro avg 0.4574 0.4303 0.3952 569
100
+ weighted avg 0.4600 0.4376 0.4012 569
101
  |
102
+ | 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
103
+
104
+ 0 0.7600 0.3654 0.4935 52
105
+ 1 0.7000 0.9333 0.8000 60
106
+ 2 0.5312 0.3333 0.4096 51
107
+ 3 0.9333 0.2545 0.4000 55
108
+ 4 0.6866 0.8214 0.7480 56
109
+ 5 0.7778 0.7500 0.7636 56
110
+ 6 0.5600 0.6667 0.6087 63
111
+ 7 0.5833 1.0000 0.7368 56
112
+ 8 0.6122 0.5000 0.5505 60
113
+ 9 0.6842 0.8667 0.7647 60
114
+
115
+ accuracy 0.6573 569
116
+ macro avg 0.6829 0.6491 0.6275 569
117
+ weighted avg 0.6813 0.6573 0.6322 569
118
  |
119
+ | 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
120
+
121
+ 0 0.6800 0.6538 0.6667 52
122
+ 1 0.7671 0.9333 0.8421 60
123
+ 2 0.7838 0.5686 0.6591 51
124
+ 3 0.8148 0.4000 0.5366 55
125
+ 4 0.8000 0.8571 0.8276 56
126
+ 5 0.8000 0.7857 0.7928 56
127
+ 6 0.8627 0.6984 0.7719 63
128
+ 7 0.7237 0.9821 0.8333 56
129
+ 8 0.6981 0.6167 0.6549 60
130
+ 9 0.6437 0.9333 0.7619 60
131
+
132
+ accuracy 0.7469 569
133
+ macro avg 0.7574 0.7429 0.7347 569
134
+ weighted avg 0.7578 0.7469 0.7370 569
135
  |
136
+ | 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
137
 
138
+ 0 0.7500 0.6346 0.6875 52
139
+ 1 0.7403 0.9500 0.8321 60
140
  2 0.7273 0.6275 0.6737 51
141
+ 3 0.7742 0.4364 0.5581 55
142
+ 4 0.8197 0.8929 0.8547 56
143
+ 5 0.7719 0.7857 0.7788 56
144
+ 6 0.8361 0.8095 0.8226 63
145
+ 7 0.7826 0.9643 0.8640 56
146
+ 8 0.8750 0.5833 0.7000 60
147
+ 9 0.6471 0.9167 0.7586 60
148
 
149
  accuracy 0.7645 569
150
+ macro avg 0.7724 0.7601 0.7530 569
151
+ weighted avg 0.7734 0.7645 0.7556 569
152
  |
153
+ | 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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
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
+ 2 0.7708 0.7255 0.7475 51
243
+ 3 0.7955 0.6364 0.7071 55
244
+ 4 0.9074 0.8750 0.8909 56
245
+ 5 0.8214 0.8214 0.8214 56
246
+ 6 0.8382 0.9048 0.8702 63
247
+ 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
+ 2 0.7551 0.7255 0.7400 51
260
+ 3 0.7955 0.6364 0.7071 55
261
+ 4 0.9245 0.8750 0.8991 56
262
+ 5 0.8364 0.8214 0.8288 56
263
+ 6 0.8750 0.8889 0.8819 63
264
+ 7 0.8710 0.9643 0.9153 56
265
+ 8 0.8372 0.6000 0.6990 60
266
+ 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
+ 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
+ 3 0.8182 0.6545 0.7273 55
295
+ 4 0.9259 0.8929 0.9091 56
296
+ 5 0.8214 0.8214 0.8214 56
297
+ 6 0.8261 0.9048 0.8636 63
298
+ 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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