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dennisjooo
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emotion_classification

Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results
Model card Files Community
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  1. README.md +110 -103
  2. config.json +1 -1
  3. pytorch_model.bin +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -4,7 +4,7 @@ base_model: google/vit-base-patch16-224-in21k
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  tags:
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  - generated_from_trainer
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  datasets:
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- - FastJobs/Visual_Emotional_Analysis
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  metrics:
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  - accuracy
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  - precision
@@ -16,65 +16,53 @@ 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: FastJobs/Visual_Emotional_Analysis
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- type: FastJobs/Visual_Emotional_Analysis
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- config: FastJobs--Visual_Emotional_Analysis
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  split: train
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- args: FastJobs--Visual_Emotional_Analysis
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.675
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  - name: Precision
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  type: precision
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- value: 0.6854354001733034
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  - name: F1
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  type: f1
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- value: 0.6750572520063745
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  ---
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36
- # Emotion Classification
 
37
 
38
- This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
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- on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
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-
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- In theory, the accuracy for a random guess on this dataset is 0.1429.
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43
  It achieves the following results on the evaluation set:
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- - Loss: 1.0683
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- - Accuracy: 0.675
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- - Precision: 0.6854
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- - F1: 0.6751
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49
  ## Model description
50
 
51
- The Vision Transformer base version trained on ImageNet-21K released by Google.
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- Further details can be found on their [repo](https://huggingface.co/google/vit-base-patch16-224-in21k).
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-
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- ## Training and evaluation data
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56
- ### Data Split
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- Used a 4:1 ratio for training and development sets and a random seed of 42.
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- Also used a seed of 42 for batching the data, completely unrelated lol.
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61
- ### Pre-processing Augmentation
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-
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- The main pre-processing phase for both training and evaluation includes:
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- - Bilinear interpolation to resize the image to (224, 224, 3) because it uses ImageNet images to train the original model
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- - Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5] just like the original model
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67
- Other than the aforementioned pre-processing, the training set was augmented using:
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- - Random horizontal & vertical flip
69
- - Color jitter
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- - Random resized crop
71
 
72
  ## Training procedure
73
 
74
  ### Training hyperparameters
75
 
76
  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
@@ -87,78 +75,97 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
89
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
90
- | 2.0804 | 1.0 | 10 | 2.0881 | 0.1437 | 0.2313 | 0.1165 |
91
- | 2.0839 | 2.0 | 20 | 2.0846 | 0.1562 | 0.1772 | 0.1250 |
92
- | 2.072 | 3.0 | 30 | 2.0786 | 0.1562 | 0.1835 | 0.1251 |
93
- | 2.0676 | 4.0 | 40 | 2.0702 | 0.1562 | 0.2213 | 0.1265 |
94
- | 2.053 | 5.0 | 50 | 2.0586 | 0.1625 | 0.2289 | 0.1330 |
95
- | 2.0346 | 6.0 | 60 | 2.0390 | 0.1938 | 0.3508 | 0.1830 |
96
- | 2.0072 | 7.0 | 70 | 2.0080 | 0.2437 | 0.3131 | 0.2285 |
97
- | 1.9672 | 8.0 | 80 | 1.9506 | 0.325 | 0.3516 | 0.3209 |
98
- | 1.8907 | 9.0 | 90 | 1.8587 | 0.3438 | 0.4010 | 0.3361 |
99
- | 1.7841 | 10.0 | 100 | 1.7300 | 0.3937 | 0.4617 | 0.3860 |
100
- | 1.6688 | 11.0 | 110 | 1.6084 | 0.4625 | 0.4958 | 0.4402 |
101
- | 1.5803 | 12.0 | 120 | 1.5305 | 0.4875 | 0.5327 | 0.4661 |
102
- | 1.5069 | 13.0 | 130 | 1.4577 | 0.5437 | 0.5171 | 0.5126 |
103
- | 1.4353 | 14.0 | 140 | 1.3955 | 0.55 | 0.6004 | 0.5380 |
104
- | 1.3913 | 15.0 | 150 | 1.3353 | 0.5437 | 0.6508 | 0.4995 |
105
- | 1.3551 | 16.0 | 160 | 1.2874 | 0.5563 | 0.5251 | 0.5201 |
106
- | 1.2889 | 17.0 | 170 | 1.2618 | 0.5687 | 0.5829 | 0.5475 |
107
- | 1.2387 | 18.0 | 180 | 1.2455 | 0.5687 | 0.5723 | 0.5587 |
108
- | 1.1977 | 19.0 | 190 | 1.2210 | 0.5875 | 0.6221 | 0.5858 |
109
- | 1.1447 | 20.0 | 200 | 1.1909 | 0.6 | 0.6153 | 0.5840 |
110
- | 1.0959 | 21.0 | 210 | 1.1918 | 0.5813 | 0.5896 | 0.5609 |
111
- | 1.0657 | 22.0 | 220 | 1.1343 | 0.625 | 0.6352 | 0.6184 |
112
- | 0.9869 | 23.0 | 230 | 1.1309 | 0.625 | 0.6549 | 0.6258 |
113
- | 0.9576 | 24.0 | 240 | 1.1071 | 0.6312 | 0.6373 | 0.6280 |
114
- | 0.9234 | 25.0 | 250 | 1.1407 | 0.6312 | 0.6469 | 0.6279 |
115
- | 0.876 | 26.0 | 260 | 1.2006 | 0.5625 | 0.6040 | 0.5514 |
116
- | 0.8969 | 27.0 | 270 | 1.1007 | 0.6125 | 0.6290 | 0.6121 |
117
- | 0.8066 | 28.0 | 280 | 1.1208 | 0.6 | 0.6650 | 0.5971 |
118
- | 0.7579 | 29.0 | 290 | 1.1328 | 0.6125 | 0.6625 | 0.6035 |
119
- | 0.7581 | 30.0 | 300 | 1.1039 | 0.6125 | 0.6401 | 0.6121 |
120
- | 0.7164 | 31.0 | 310 | 1.0862 | 0.65 | 0.6723 | 0.6494 |
121
- | 0.7075 | 32.0 | 320 | 1.0575 | 0.65 | 0.6683 | 0.6485 |
122
- | 0.6655 | 33.0 | 330 | 1.1186 | 0.6125 | 0.6483 | 0.6134 |
123
- | 0.5947 | 34.0 | 340 | 1.1133 | 0.625 | 0.6439 | 0.6272 |
124
- | 0.5813 | 35.0 | 350 | 1.1071 | 0.6312 | 0.6735 | 0.6337 |
125
- | 0.6322 | 36.0 | 360 | 1.0839 | 0.6312 | 0.6591 | 0.6324 |
126
- | 0.561 | 37.0 | 370 | 1.1040 | 0.625 | 0.6425 | 0.6220 |
127
- | 0.558 | 38.0 | 380 | 1.0727 | 0.6125 | 0.6255 | 0.6112 |
128
- | 0.5372 | 39.0 | 390 | 1.1417 | 0.6312 | 0.6545 | 0.6292 |
129
- | 0.5146 | 40.0 | 400 | 1.0967 | 0.6312 | 0.6645 | 0.6285 |
130
- | 0.4968 | 41.0 | 410 | 1.1187 | 0.6312 | 0.6543 | 0.6316 |
131
- | 0.4593 | 42.0 | 420 | 1.0683 | 0.675 | 0.6854 | 0.6751 |
132
- | 0.4392 | 43.0 | 430 | 1.0937 | 0.6375 | 0.6481 | 0.6374 |
133
- | 0.4503 | 44.0 | 440 | 1.1320 | 0.625 | 0.6536 | 0.6255 |
134
- | 0.3918 | 45.0 | 450 | 1.1218 | 0.6312 | 0.6464 | 0.6312 |
135
- | 0.4236 | 46.0 | 460 | 1.2074 | 0.5938 | 0.6188 | 0.5911 |
136
- | 0.3858 | 47.0 | 470 | 1.1769 | 0.5813 | 0.6106 | 0.5809 |
137
- | 0.392 | 48.0 | 480 | 1.1572 | 0.625 | 0.6381 | 0.6216 |
138
- | 0.3708 | 49.0 | 490 | 1.2293 | 0.6 | 0.6388 | 0.5953 |
139
- | 0.3346 | 50.0 | 500 | 1.2205 | 0.5938 | 0.6188 | 0.5943 |
140
- | 0.3831 | 51.0 | 510 | 1.2875 | 0.5875 | 0.5982 | 0.5845 |
141
- | 0.4161 | 52.0 | 520 | 1.2355 | 0.5938 | 0.6421 | 0.5799 |
142
- | 0.3736 | 53.0 | 530 | 1.2361 | 0.6062 | 0.6301 | 0.6006 |
143
- | 0.3278 | 54.0 | 540 | 1.1670 | 0.6312 | 0.6520 | 0.6286 |
144
- | 0.3295 | 55.0 | 550 | 1.1807 | 0.6438 | 0.6712 | 0.6457 |
145
- | 0.3357 | 56.0 | 560 | 1.2007 | 0.625 | 0.6279 | 0.6239 |
146
- | 0.3169 | 57.0 | 570 | 1.2314 | 0.5938 | 0.6257 | 0.5942 |
147
- | 0.3193 | 58.0 | 580 | 1.2068 | 0.6188 | 0.6397 | 0.6208 |
148
- | 0.3128 | 59.0 | 590 | 1.2753 | 0.5875 | 0.5919 | 0.5760 |
149
- | 0.3077 | 60.0 | 600 | 1.2154 | 0.625 | 0.6432 | 0.6238 |
150
- | 0.2751 | 61.0 | 610 | 1.2596 | 0.6125 | 0.6216 | 0.6099 |
151
- | 0.2921 | 62.0 | 620 | 1.2716 | 0.6188 | 0.6467 | 0.6189 |
152
- | 0.2939 | 63.0 | 630 | 1.2213 | 0.625 | 0.6350 | 0.6264 |
153
- | 0.2732 | 64.0 | 640 | 1.3456 | 0.5938 | 0.6189 | 0.5897 |
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- | 0.2806 | 65.0 | 650 | 1.2491 | 0.6188 | 0.6393 | 0.6162 |
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- | 0.2453 | 66.0 | 660 | 1.2312 | 0.6188 | 0.6465 | 0.6195 |
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- | 0.3077 | 67.0 | 670 | 1.2356 | 0.6375 | 0.6564 | 0.6373 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
157
 
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159
  ### Framework versions
160
 
161
- - Transformers 4.33.0
162
  - Pytorch 2.0.0
163
- - Datasets 2.1.0
164
  - Tokenizers 0.13.3
 
4
  tags:
5
  - generated_from_trainer
6
  datasets:
7
+ - imagefolder
8
  metrics:
9
  - accuracy
10
  - precision
 
16
  name: Image Classification
17
  type: image-classification
18
  dataset:
19
+ name: imagefolder
20
+ type: imagefolder
21
+ config: default
22
  split: train
23
+ args: default
24
  metrics:
25
  - name: Accuracy
26
  type: accuracy
27
+ value: 0.63125
28
  - name: Precision
29
  type: precision
30
+ value: 0.6580684399341683
31
  - name: F1
32
  type: f1
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+ value: 0.6375321878900636
34
  ---
35
 
36
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
37
+ should probably proofread and complete it, then remove this comment. -->
38
 
39
+ # emotion_classification
 
 
 
40
 
41
+ 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.
42
  It achieves the following results on the evaluation set:
43
+ - Loss: 1.1145
44
+ - Accuracy: 0.6312
45
+ - Precision: 0.6581
46
+ - F1: 0.6375
47
 
48
  ## Model description
49
 
50
+ More information needed
 
 
 
51
 
52
+ ## Intended uses & limitations
53
 
54
+ More information needed
 
55
 
56
+ ## Training and evaluation data
 
 
 
 
57
 
58
+ More information needed
 
 
 
59
 
60
  ## Training procedure
61
 
62
  ### Training hyperparameters
63
 
64
  The following hyperparameters were used during training:
65
+ - learning_rate: 3e-05
66
  - train_batch_size: 64
67
  - eval_batch_size: 64
68
  - seed: 42
 
75
 
76
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
77
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
78
+ | 2.0848 | 1.0 | 10 | 2.0806 | 0.1625 | 0.1527 | 0.1483 |
79
+ | 2.0824 | 2.0 | 20 | 2.0784 | 0.1688 | 0.1556 | 0.1538 |
80
+ | 2.0785 | 3.0 | 30 | 2.0748 | 0.175 | 0.1612 | 0.1606 |
81
+ | 2.0709 | 4.0 | 40 | 2.0698 | 0.1812 | 0.1684 | 0.1661 |
82
+ | 2.067 | 5.0 | 50 | 2.0635 | 0.1812 | 0.1787 | 0.1697 |
83
+ | 2.0554 | 6.0 | 60 | 2.0553 | 0.2 | 0.1958 | 0.1893 |
84
+ | 2.0461 | 7.0 | 70 | 2.0438 | 0.2313 | 0.2434 | 0.2272 |
85
+ | 2.0263 | 8.0 | 80 | 2.0260 | 0.2437 | 0.2763 | 0.2472 |
86
+ | 1.9963 | 9.0 | 90 | 1.9959 | 0.275 | 0.3073 | 0.2780 |
87
+ | 1.9512 | 10.0 | 100 | 1.9435 | 0.3312 | 0.3481 | 0.3307 |
88
+ | 1.8885 | 11.0 | 110 | 1.8610 | 0.4313 | 0.4426 | 0.4138 |
89
+ | 1.7908 | 12.0 | 120 | 1.7604 | 0.4688 | 0.4485 | 0.4243 |
90
+ | 1.6944 | 13.0 | 130 | 1.6677 | 0.4813 | 0.4369 | 0.4349 |
91
+ | 1.6245 | 14.0 | 140 | 1.6105 | 0.4625 | 0.4071 | 0.4124 |
92
+ | 1.5745 | 15.0 | 150 | 1.5671 | 0.5062 | 0.4551 | 0.4690 |
93
+ | 1.5132 | 16.0 | 160 | 1.5169 | 0.4688 | 0.4481 | 0.4201 |
94
+ | 1.471 | 17.0 | 170 | 1.4772 | 0.4813 | 0.4203 | 0.4404 |
95
+ | 1.4272 | 18.0 | 180 | 1.4426 | 0.4938 | 0.4453 | 0.4496 |
96
+ | 1.3896 | 19.0 | 190 | 1.4153 | 0.4813 | 0.4409 | 0.4370 |
97
+ | 1.3347 | 20.0 | 200 | 1.3976 | 0.5062 | 0.4694 | 0.4662 |
98
+ | 1.3145 | 21.0 | 210 | 1.3840 | 0.4813 | 0.4459 | 0.4366 |
99
+ | 1.3319 | 22.0 | 220 | 1.3511 | 0.5062 | 0.4867 | 0.4655 |
100
+ | 1.2438 | 23.0 | 230 | 1.3186 | 0.5312 | 0.5804 | 0.4945 |
101
+ | 1.2202 | 24.0 | 240 | 1.3012 | 0.5375 | 0.5342 | 0.5023 |
102
+ | 1.1838 | 25.0 | 250 | 1.2879 | 0.5563 | 0.6162 | 0.5295 |
103
+ | 1.1448 | 26.0 | 260 | 1.2534 | 0.5687 | 0.5631 | 0.5456 |
104
+ | 1.113 | 27.0 | 270 | 1.2398 | 0.55 | 0.5645 | 0.5359 |
105
+ | 1.0862 | 28.0 | 280 | 1.2357 | 0.5437 | 0.6075 | 0.5143 |
106
+ | 1.0837 | 29.0 | 290 | 1.2095 | 0.5687 | 0.5653 | 0.5471 |
107
+ | 1.0609 | 30.0 | 300 | 1.2095 | 0.5437 | 0.5729 | 0.5393 |
108
+ | 1.0112 | 31.0 | 310 | 1.1859 | 0.575 | 0.5989 | 0.5490 |
109
+ | 0.9584 | 32.0 | 320 | 1.1683 | 0.5875 | 0.6019 | 0.5777 |
110
+ | 0.941 | 33.0 | 330 | 1.1649 | 0.5938 | 0.6083 | 0.5875 |
111
+ | 0.904 | 34.0 | 340 | 1.1896 | 0.5875 | 0.6078 | 0.5720 |
112
+ | 0.921 | 35.0 | 350 | 1.1662 | 0.6062 | 0.6352 | 0.5975 |
113
+ | 0.9026 | 36.0 | 360 | 1.1441 | 0.5875 | 0.5981 | 0.5841 |
114
+ | 0.8217 | 37.0 | 370 | 1.1602 | 0.5813 | 0.6098 | 0.5779 |
115
+ | 0.8292 | 38.0 | 380 | 1.2140 | 0.5437 | 0.5588 | 0.5258 |
116
+ | 0.8017 | 39.0 | 390 | 1.1545 | 0.5563 | 0.5459 | 0.5294 |
117
+ | 0.7787 | 40.0 | 400 | 1.1358 | 0.6062 | 0.6300 | 0.5948 |
118
+ | 0.7473 | 41.0 | 410 | 1.1285 | 0.5813 | 0.5996 | 0.5779 |
119
+ | 0.6941 | 42.0 | 420 | 1.1311 | 0.575 | 0.5982 | 0.5757 |
120
+ | 0.7009 | 43.0 | 430 | 1.1296 | 0.6125 | 0.6371 | 0.6076 |
121
+ | 0.6537 | 44.0 | 440 | 1.0996 | 0.5813 | 0.5866 | 0.5684 |
122
+ | 0.6524 | 45.0 | 450 | 1.1477 | 0.5875 | 0.6077 | 0.5813 |
123
+ | 0.674 | 46.0 | 460 | 1.1063 | 0.6188 | 0.6322 | 0.6127 |
124
+ | 0.5999 | 47.0 | 470 | 1.1077 | 0.6 | 0.6035 | 0.5951 |
125
+ | 0.6194 | 48.0 | 480 | 1.1249 | 0.5813 | 0.5936 | 0.5805 |
126
+ | 0.595 | 49.0 | 490 | 1.1331 | 0.6 | 0.5955 | 0.5876 |
127
+ | 0.5403 | 50.0 | 500 | 1.1577 | 0.5875 | 0.6010 | 0.5781 |
128
+ | 0.5932 | 51.0 | 510 | 1.1352 | 0.5938 | 0.6214 | 0.5851 |
129
+ | 0.621 | 52.0 | 520 | 1.0893 | 0.6062 | 0.6044 | 0.6007 |
130
+ | 0.5157 | 53.0 | 530 | 1.1382 | 0.6125 | 0.6173 | 0.6075 |
131
+ | 0.5318 | 54.0 | 540 | 1.1402 | 0.6 | 0.6158 | 0.5970 |
132
+ | 0.4757 | 55.0 | 550 | 1.1668 | 0.5938 | 0.6096 | 0.5930 |
133
+ | 0.4826 | 56.0 | 560 | 1.1506 | 0.6062 | 0.6367 | 0.6051 |
134
+ | 0.5058 | 57.0 | 570 | 1.1857 | 0.5875 | 0.5873 | 0.5767 |
135
+ | 0.4791 | 58.0 | 580 | 1.1618 | 0.5813 | 0.5670 | 0.5587 |
136
+ | 0.4322 | 59.0 | 590 | 1.2007 | 0.5625 | 0.5628 | 0.5532 |
137
+ | 0.442 | 60.0 | 600 | 1.1862 | 0.5875 | 0.5681 | 0.5560 |
138
+ | 0.431 | 61.0 | 610 | 1.1145 | 0.6312 | 0.6581 | 0.6375 |
139
+ | 0.4131 | 62.0 | 620 | 1.2081 | 0.575 | 0.5912 | 0.5705 |
140
+ | 0.3911 | 63.0 | 630 | 1.1380 | 0.6062 | 0.6043 | 0.5988 |
141
+ | 0.4281 | 64.0 | 640 | 1.1189 | 0.6188 | 0.6157 | 0.6138 |
142
+ | 0.385 | 65.0 | 650 | 1.2177 | 0.5625 | 0.5888 | 0.5615 |
143
+ | 0.398 | 66.0 | 660 | 1.2204 | 0.6 | 0.6321 | 0.6008 |
144
+ | 0.4821 | 67.0 | 670 | 1.2037 | 0.5938 | 0.6065 | 0.5804 |
145
+ | 0.4127 | 68.0 | 680 | 1.1473 | 0.6 | 0.6193 | 0.5996 |
146
+ | 0.4062 | 69.0 | 690 | 1.2160 | 0.5938 | 0.5950 | 0.5806 |
147
+ | 0.3906 | 70.0 | 700 | 1.1763 | 0.5938 | 0.6421 | 0.6034 |
148
+ | 0.352 | 71.0 | 710 | 1.2355 | 0.5687 | 0.5836 | 0.5613 |
149
+ | 0.3801 | 72.0 | 720 | 1.1623 | 0.5813 | 0.5800 | 0.5789 |
150
+ | 0.333 | 73.0 | 730 | 1.1770 | 0.5875 | 0.5920 | 0.5851 |
151
+ | 0.3562 | 74.0 | 740 | 1.2140 | 0.5875 | 0.6367 | 0.5917 |
152
+ | 0.3403 | 75.0 | 750 | 1.1679 | 0.6 | 0.6209 | 0.6044 |
153
+ | 0.3456 | 76.0 | 760 | 1.2496 | 0.5625 | 0.5465 | 0.5409 |
154
+ | 0.3331 | 77.0 | 770 | 1.1975 | 0.575 | 0.6042 | 0.5759 |
155
+ | 0.3408 | 78.0 | 780 | 1.2381 | 0.575 | 0.5606 | 0.5565 |
156
+ | 0.2964 | 79.0 | 790 | 1.1792 | 0.6 | 0.6204 | 0.6009 |
157
+ | 0.2833 | 80.0 | 800 | 1.1840 | 0.6 | 0.6059 | 0.5933 |
158
+ | 0.2875 | 81.0 | 810 | 1.2024 | 0.5875 | 0.5920 | 0.5841 |
159
+ | 0.327 | 82.0 | 820 | 1.2190 | 0.5813 | 0.5799 | 0.5728 |
160
+ | 0.3027 | 83.0 | 830 | 1.2520 | 0.5813 | 0.5682 | 0.5704 |
161
+ | 0.2731 | 84.0 | 840 | 1.2167 | 0.5875 | 0.6021 | 0.5847 |
162
+ | 0.2821 | 85.0 | 850 | 1.2805 | 0.575 | 0.5659 | 0.5527 |
163
+ | 0.3192 | 86.0 | 860 | 1.2453 | 0.5625 | 0.5585 | 0.5575 |
164
 
165
 
166
  ### Framework versions
167
 
168
+ - Transformers 4.33.1
169
  - Pytorch 2.0.0
170
+ - Datasets 2.14.5
171
  - Tokenizers 0.13.3
config.json CHANGED
@@ -40,5 +40,5 @@
40
  "problem_type": "single_label_classification",
41
  "qkv_bias": true,
42
  "torch_dtype": "float32",
43
- "transformers_version": "4.33.0"
44
  }
 
40
  "problem_type": "single_label_classification",
41
  "qkv_bias": true,
42
  "torch_dtype": "float32",
43
+ "transformers_version": "4.33.1"
44
  }
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
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