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Raihan004
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Action_agent

Image Classification
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Safetensors
vit
Generated from Trainer
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  1. README.md +247 -71
  2. model.safetensors +1 -1
  3. runs/May07_16-32-31_9de76f366adc/events.out.tfevents.1715099551.9de76f366adc.34.0 +3 -0
  4. runs/May07_16-34-51_9de76f366adc/events.out.tfevents.1715099692.9de76f366adc.34.1 +3 -0
  5. runs/May07_16-34-51_9de76f366adc/events.out.tfevents.1715099790.9de76f366adc.34.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -2,7 +2,6 @@
2
  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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- - image-classification
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  - generated_from_trainer
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  datasets:
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  - imagefolder
@@ -15,7 +14,7 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: agent_action_class
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  type: imagefolder
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  config: default
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  split: train
@@ -23,7 +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: 0.8142857142857143
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  ---
28
 
29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,10 +30,28 @@ should probably proofread and complete it, then remove this comment. -->
31
 
32
  # Action_agent
33
 
34
- 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 agent_action_class dataset.
35
  It achieves the following results on the evaluation set:
36
- - Loss: 0.6352
37
- - Accuracy: 0.8143
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
38
 
39
  ## Model description
40
 
@@ -59,74 +76,233 @@ The following hyperparameters were used during training:
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  - seed: 42
60
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
61
  - lr_scheduler_type: linear
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- - num_epochs: 20
63
 
64
  ### Training results
65
 
66
- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
67
- |:-------------:|:-----:|:----:|:---------------:|:--------:|
68
- | 2.1987 | 0.32 | 100 | 2.1640 | 0.3914 |
69
- | 1.9807 | 0.64 | 200 | 1.9169 | 0.6143 |
70
- | 1.6738 | 0.96 | 300 | 1.6148 | 0.72 |
71
- | 1.4828 | 1.27 | 400 | 1.3861 | 0.7705 |
72
- | 1.2768 | 1.59 | 500 | 1.2412 | 0.7590 |
73
- | 1.1759 | 1.91 | 600 | 1.1169 | 0.7914 |
74
- | 1.0314 | 2.23 | 700 | 1.0599 | 0.7762 |
75
- | 0.9702 | 2.55 | 800 | 0.9640 | 0.8105 |
76
- | 0.9559 | 2.87 | 900 | 0.9138 | 0.8076 |
77
- | 0.858 | 3.18 | 1000 | 0.8605 | 0.8248 |
78
- | 0.7858 | 3.5 | 1100 | 0.8164 | 0.8371 |
79
- | 0.7898 | 3.82 | 1200 | 0.7917 | 0.8333 |
80
- | 0.6909 | 4.14 | 1300 | 0.7995 | 0.8038 |
81
- | 0.6619 | 4.46 | 1400 | 0.8194 | 0.7829 |
82
- | 0.6457 | 4.78 | 1500 | 0.7536 | 0.8086 |
83
- | 0.6155 | 5.1 | 1600 | 0.7212 | 0.8257 |
84
- | 0.5511 | 5.41 | 1700 | 0.7274 | 0.8095 |
85
- | 0.5486 | 5.73 | 1800 | 0.7048 | 0.8286 |
86
- | 0.5679 | 6.05 | 1900 | 0.7124 | 0.8181 |
87
- | 0.4914 | 6.37 | 2000 | 0.7277 | 0.8010 |
88
- | 0.525 | 6.69 | 2100 | 0.6971 | 0.8124 |
89
- | 0.5081 | 7.01 | 2200 | 0.6869 | 0.8162 |
90
- | 0.5072 | 7.32 | 2300 | 0.6837 | 0.8076 |
91
- | 0.4702 | 7.64 | 2400 | 0.6736 | 0.8152 |
92
- | 0.4303 | 7.96 | 2500 | 0.6693 | 0.8105 |
93
- | 0.3916 | 8.28 | 2600 | 0.6487 | 0.8238 |
94
- | 0.4002 | 8.6 | 2700 | 0.6661 | 0.8162 |
95
- | 0.3965 | 8.92 | 2800 | 0.6611 | 0.8143 |
96
- | 0.3946 | 9.24 | 2900 | 0.6523 | 0.8143 |
97
- | 0.3794 | 9.55 | 3000 | 0.6616 | 0.8048 |
98
- | 0.3257 | 9.87 | 3100 | 0.6717 | 0.8029 |
99
- | 0.4175 | 10.19 | 3200 | 0.6530 | 0.8057 |
100
- | 0.3559 | 10.51 | 3300 | 0.6883 | 0.7886 |
101
- | 0.3824 | 10.83 | 3400 | 0.6611 | 0.8 |
102
- | 0.3589 | 11.15 | 3500 | 0.6659 | 0.8019 |
103
- | 0.3299 | 11.46 | 3600 | 0.6819 | 0.7962 |
104
- | 0.3736 | 11.78 | 3700 | 0.6405 | 0.8114 |
105
- | 0.3576 | 12.1 | 3800 | 0.6725 | 0.7962 |
106
- | 0.3454 | 12.42 | 3900 | 0.7025 | 0.7943 |
107
- | 0.3049 | 12.74 | 4000 | 0.6439 | 0.8133 |
108
- | 0.3363 | 13.06 | 4100 | 0.6352 | 0.8143 |
109
- | 0.3273 | 13.38 | 4200 | 0.6795 | 0.7886 |
110
- | 0.283 | 13.69 | 4300 | 0.6705 | 0.8 |
111
- | 0.2607 | 14.01 | 4400 | 0.6732 | 0.7914 |
112
- | 0.3174 | 14.33 | 4500 | 0.6691 | 0.8048 |
113
- | 0.3189 | 14.65 | 4600 | 0.6602 | 0.8038 |
114
- | 0.2862 | 14.97 | 4700 | 0.6801 | 0.7933 |
115
- | 0.2895 | 15.29 | 4800 | 0.6579 | 0.8038 |
116
- | 0.263 | 15.61 | 4900 | 0.6688 | 0.8 |
117
- | 0.3214 | 15.92 | 5000 | 0.6547 | 0.8057 |
118
- | 0.2867 | 16.24 | 5100 | 0.6775 | 0.7924 |
119
- | 0.2242 | 16.56 | 5200 | 0.6378 | 0.8086 |
120
- | 0.2839 | 16.88 | 5300 | 0.6761 | 0.7990 |
121
- | 0.2424 | 17.2 | 5400 | 0.6386 | 0.8124 |
122
- | 0.2666 | 17.52 | 5500 | 0.6493 | 0.8133 |
123
- | 0.2259 | 17.83 | 5600 | 0.6514 | 0.8048 |
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- | 0.2533 | 18.15 | 5700 | 0.6676 | 0.8 |
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- | 0.2697 | 18.47 | 5800 | 0.6705 | 0.8010 |
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- | 0.2558 | 18.79 | 5900 | 0.6750 | 0.8076 |
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- | 0.2469 | 19.11 | 6000 | 0.6751 | 0.7990 |
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- | 0.284 | 19.43 | 6100 | 0.6738 | 0.7981 |
129
- | 0.2534 | 19.75 | 6200 | 0.6758 | 0.8019 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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131
 
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  ### Framework versions
 
2
  license: apache-2.0
3
  base_model: google/vit-base-patch16-224-in21k
4
  tags:
 
5
  - generated_from_trainer
6
  datasets:
7
  - imagefolder
 
14
  name: Image Classification
15
  type: image-classification
16
  dataset:
17
+ name: imagefolder
18
  type: imagefolder
19
  config: default
20
  split: train
 
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.81195079086116
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
30
 
31
  # Action_agent
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: 0.9874
36
+ - Accuracy: 0.8120
37
+ - 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]]
38
+ - Classification Report: precision recall f1-score support
39
+
40
+ 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
48
+ 8 0.8500 0.5667 0.6800 60
49
+ 9 0.7568 0.9333 0.8358 60
50
+
51
+ accuracy 0.8120 569
52
+ macro avg 0.8134 0.8106 0.8072 569
53
+ weighted avg 0.8145 0.8120 0.8082 569
54
+
55
 
56
  ## Model description
57
 
 
76
  - seed: 42
77
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
78
  - lr_scheduler_type: linear
79
+ - num_epochs: 10
80
 
81
  ### Training results
82
 
83
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Confusion Matrix | Classification Report |
84
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
85
+ | 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
+
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+ 0 0.0000 0.0000 0.0000 52
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+ 1 0.4860 0.8667 0.6228 60
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+ 2 0.2500 0.1176 0.1600 51
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+ 3 0.4118 0.2545 0.3146 55
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+ 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
95
+ 8 0.3919 0.4833 0.4328 60
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+ 9 0.6875 0.3667 0.4783 60
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+
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+ 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
103
+
104
+ 0 0.7500 0.1154 0.2000 52
105
+ 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
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+ 4 0.5281 0.8393 0.6483 56
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+ 5 0.8649 0.5714 0.6882 56
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+ 6 0.7077 0.7302 0.7188 63
111
+ 7 0.5521 0.9464 0.6974 56
112
+ 8 0.7250 0.4833 0.5800 60
113
+ 9 0.7778 0.8167 0.7967 60
114
+
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+ 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
120
+
121
+ 0 0.8667 0.5000 0.6341 52
122
+ 1 0.7143 0.9167 0.8029 60
123
+ 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
126
+ 5 0.8654 0.8036 0.8333 56
127
+ 6 0.7705 0.7460 0.7581 63
128
+ 7 0.6974 0.9464 0.8030 56
129
+ 8 0.7400 0.6167 0.6727 60
130
+ 9 0.7237 0.9167 0.8088 60
131
+
132
+ accuracy 0.7487 569
133
+ macro avg 0.7618 0.7434 0.7385 569
134
+ weighted avg 0.7601 0.7487 0.7409 569
135
+ |
136
+ | 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
137
+
138
+ 0 0.7955 0.6731 0.7292 52
139
+ 1 0.7639 0.9167 0.8333 60
140
+ 2 0.7273 0.6275 0.6737 51
141
+ 3 0.8182 0.4909 0.6136 55
142
+ 4 0.7692 0.8929 0.8264 56
143
+ 5 0.8235 0.7500 0.7850 56
144
+ 6 0.8000 0.8254 0.8125 63
145
+ 7 0.7429 0.9286 0.8254 56
146
+ 8 0.8049 0.5500 0.6535 60
147
+ 9 0.6786 0.9500 0.7917 60
148
+
149
+ accuracy 0.7645 569
150
+ macro avg 0.7724 0.7605 0.7544 569
151
+ weighted avg 0.7724 0.7645 0.7564 569
152
+ |
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
154
+
155
+ 0 0.7447 0.6731 0.7071 52
156
+ 1 0.7467 0.9333 0.8296 60
157
+ 2 0.8158 0.6078 0.6966 51
158
+ 3 0.7879 0.4727 0.5909 55
159
+ 4 0.8621 0.8929 0.8772 56
160
+ 5 0.8491 0.8036 0.8257 56
161
+ 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
167
+ macro avg 0.7817 0.7619 0.7561 569
168
+ weighted avg 0.7810 0.7663 0.7578 569
169
+ |
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
171
+
172
+ 0 0.7708 0.7115 0.7400 52
173
+ 1 0.7910 0.8833 0.8346 60
174
+ 2 0.7619 0.6275 0.6882 51
175
+ 3 0.7907 0.6182 0.6939 55
176
+ 4 0.8361 0.9107 0.8718 56
177
+ 5 0.8214 0.8214 0.8214 56
178
+ 6 0.8154 0.8413 0.8281 63
179
+ 7 0.7429 0.9286 0.8254 56
180
+ 8 0.8250 0.5500 0.6600 60
181
+ 9 0.7273 0.9333 0.8175 60
182
+
183
+ accuracy 0.7856 569
184
+ macro avg 0.7882 0.7826 0.7781 569
185
+ weighted avg 0.7888 0.7856 0.7798 569
186
+ |
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
188
+
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
192
+ 3 0.7632 0.5273 0.6237 55
193
+ 4 0.8667 0.9286 0.8966 56
194
+ 5 0.8364 0.8214 0.8288 56
195
+ 6 0.8308 0.8571 0.8438 63
196
+ 7 0.8525 0.9286 0.8889 56
197
+ 8 0.8649 0.5333 0.6598 60
198
+ 9 0.6706 0.9500 0.7862 60
199
+
200
+ accuracy 0.7926 569
201
+ macro avg 0.7997 0.7903 0.7854 569
202
+ weighted avg 0.7997 0.7926 0.7862 569
203
+ |
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
205
+
206
+ 0 0.7708 0.7115 0.7400 52
207
+ 1 0.7333 0.9167 0.8148 60
208
+ 2 0.7255 0.7255 0.7255 51
209
+ 3 0.8095 0.6182 0.7010 55
210
+ 4 0.8929 0.8929 0.8929 56
211
+ 5 0.8302 0.7857 0.8073 56
212
+ 6 0.8387 0.8254 0.8320 63
213
+ 7 0.8814 0.9286 0.9043 56
214
+ 8 0.7907 0.5667 0.6602 60
215
+ 9 0.7125 0.9500 0.8143 60
216
+
217
+ accuracy 0.7944 569
218
+ 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
222
+
223
+ 0 0.7500 0.7500 0.7500 52
224
+ 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
229
+ 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
232
+ 9 0.7215 0.9500 0.8201 60
233
+
234
+ accuracy 0.8049 569
235
+ macro avg 0.8090 0.8037 0.7999 569
236
+ 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
239
+
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
243
+ 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
256
+
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
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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
273
+
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
290
+
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
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295
+ 4 0.9091 0.8929 0.9009 56
296
+ 5 0.8364 0.8214 0.8288 56
297
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298
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299
+ 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
305
+ |
306
 
307
 
308
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