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dennisjooo
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emotion_classification

Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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  1. README.md +71 -111
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6625
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  - name: Precision
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  type: precision
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- value: 0.6857332900074835
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  - name: F1
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  type: f1
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- value: 0.6658368805611075
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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
@@ -40,10 +40,10 @@ 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 image_folder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1720
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- - Accuracy: 0.6625
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- - Precision: 0.6857
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- - F1: 0.6658
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  ## Model description
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@@ -75,110 +75,70 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
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- | 2.0805 | 1.0 | 10 | 2.0844 | 0.1688 | 0.1731 | 0.1670 |
79
- | 2.0876 | 2.0 | 20 | 2.0807 | 0.1938 | 0.1814 | 0.1843 |
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- | 2.0786 | 3.0 | 30 | 2.0741 | 0.1812 | 0.1658 | 0.1702 |
81
- | 2.0653 | 4.0 | 40 | 2.0663 | 0.2062 | 0.1832 | 0.1893 |
82
- | 2.0586 | 5.0 | 50 | 2.0547 | 0.2062 | 0.1817 | 0.1911 |
83
- | 2.0347 | 6.0 | 60 | 2.0343 | 0.2375 | 0.2074 | 0.2187 |
84
- | 2.009 | 7.0 | 70 | 2.0039 | 0.2875 | 0.4007 | 0.2750 |
85
- | 1.9672 | 8.0 | 80 | 1.9560 | 0.3187 | 0.3615 | 0.3006 |
86
- | 1.9015 | 9.0 | 90 | 1.8650 | 0.3688 | 0.4229 | 0.3577 |
87
- | 1.812 | 10.0 | 100 | 1.7339 | 0.4375 | 0.3925 | 0.4045 |
88
- | 1.6993 | 11.0 | 110 | 1.6196 | 0.4688 | 0.4093 | 0.4267 |
89
- | 1.6037 | 12.0 | 120 | 1.5466 | 0.475 | 0.4808 | 0.4413 |
90
- | 1.5332 | 13.0 | 130 | 1.4791 | 0.525 | 0.4749 | 0.4922 |
91
- | 1.4649 | 14.0 | 140 | 1.4201 | 0.525 | 0.4860 | 0.4948 |
92
- | 1.4142 | 15.0 | 150 | 1.3659 | 0.55 | 0.5231 | 0.5178 |
93
- | 1.3826 | 16.0 | 160 | 1.3001 | 0.575 | 0.5346 | 0.5434 |
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- | 1.3048 | 17.0 | 170 | 1.2689 | 0.5813 | 0.5381 | 0.5535 |
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- | 1.2519 | 18.0 | 180 | 1.2334 | 0.575 | 0.5816 | 0.5580 |
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- | 1.2043 | 19.0 | 190 | 1.2186 | 0.55 | 0.5739 | 0.5424 |
97
- | 1.1575 | 20.0 | 200 | 1.1711 | 0.5687 | 0.5421 | 0.5371 |
98
- | 1.0957 | 21.0 | 210 | 1.1674 | 0.5938 | 0.5764 | 0.5645 |
99
- | 1.0719 | 22.0 | 220 | 1.1473 | 0.5875 | 0.5899 | 0.5721 |
100
- | 0.9894 | 23.0 | 230 | 1.1303 | 0.6125 | 0.6507 | 0.6124 |
101
- | 0.9698 | 24.0 | 240 | 1.1010 | 0.6188 | 0.6323 | 0.6142 |
102
- | 0.9081 | 25.0 | 250 | 1.1038 | 0.5938 | 0.6074 | 0.5923 |
103
- | 0.8739 | 26.0 | 260 | 1.1383 | 0.5563 | 0.5874 | 0.5447 |
104
- | 0.8815 | 27.0 | 270 | 1.1483 | 0.6 | 0.6524 | 0.5894 |
105
- | 0.8426 | 28.0 | 280 | 1.1212 | 0.5813 | 0.6356 | 0.5703 |
106
- | 0.7614 | 29.0 | 290 | 1.1002 | 0.6188 | 0.6724 | 0.6089 |
107
- | 0.7937 | 30.0 | 300 | 1.0272 | 0.6188 | 0.6515 | 0.6135 |
108
- | 0.7379 | 31.0 | 310 | 1.0184 | 0.6062 | 0.6120 | 0.6035 |
109
- | 0.6994 | 32.0 | 320 | 1.0163 | 0.5875 | 0.5966 | 0.5772 |
110
- | 0.684 | 33.0 | 330 | 1.0420 | 0.6312 | 0.6627 | 0.6327 |
111
- | 0.605 | 34.0 | 340 | 1.0555 | 0.6312 | 0.6822 | 0.6353 |
112
- | 0.5851 | 35.0 | 350 | 1.0991 | 0.625 | 0.6941 | 0.6269 |
113
- | 0.6186 | 36.0 | 360 | 1.1196 | 0.6188 | 0.6916 | 0.6077 |
114
- | 0.5349 | 37.0 | 370 | 1.0707 | 0.6062 | 0.6123 | 0.5978 |
115
- | 0.5549 | 38.0 | 380 | 1.0161 | 0.6375 | 0.6498 | 0.6308 |
116
- | 0.577 | 39.0 | 390 | 1.1375 | 0.5813 | 0.6449 | 0.5770 |
117
- | 0.5151 | 40.0 | 400 | 1.0479 | 0.65 | 0.6691 | 0.6421 |
118
- | 0.4898 | 41.0 | 410 | 1.0835 | 0.6125 | 0.6378 | 0.6106 |
119
- | 0.4619 | 42.0 | 420 | 1.0262 | 0.6375 | 0.6596 | 0.6418 |
120
- | 0.4142 | 43.0 | 430 | 1.1238 | 0.6188 | 0.6422 | 0.6143 |
121
- | 0.4695 | 44.0 | 440 | 1.0765 | 0.65 | 0.6664 | 0.6424 |
122
- | 0.4195 | 45.0 | 450 | 1.0646 | 0.6375 | 0.6622 | 0.6357 |
123
- | 0.4144 | 46.0 | 460 | 1.1255 | 0.6 | 0.6308 | 0.6023 |
124
- | 0.3552 | 47.0 | 470 | 1.0580 | 0.6562 | 0.6639 | 0.6574 |
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- | 0.3887 | 48.0 | 480 | 1.0673 | 0.6438 | 0.6560 | 0.6421 |
126
- | 0.348 | 49.0 | 490 | 1.1828 | 0.6062 | 0.6503 | 0.6041 |
127
- | 0.3284 | 50.0 | 500 | 1.1613 | 0.5625 | 0.5756 | 0.5585 |
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- | 0.4082 | 51.0 | 510 | 1.1582 | 0.6188 | 0.6458 | 0.6154 |
129
- | 0.3929 | 52.0 | 520 | 1.1444 | 0.6188 | 0.6438 | 0.6117 |
130
- | 0.337 | 53.0 | 530 | 1.1073 | 0.6375 | 0.6497 | 0.6348 |
131
- | 0.3525 | 54.0 | 540 | 1.1750 | 0.6062 | 0.6331 | 0.6079 |
132
- | 0.3336 | 55.0 | 550 | 1.1841 | 0.6188 | 0.6435 | 0.6116 |
133
- | 0.2946 | 56.0 | 560 | 1.2258 | 0.5875 | 0.6250 | 0.5820 |
134
- | 0.332 | 57.0 | 570 | 1.1952 | 0.5938 | 0.6526 | 0.6018 |
135
- | 0.3013 | 58.0 | 580 | 1.1858 | 0.6438 | 0.6671 | 0.6465 |
136
- | 0.3035 | 59.0 | 590 | 1.1823 | 0.625 | 0.6326 | 0.6238 |
137
- | 0.3071 | 60.0 | 600 | 1.1567 | 0.6062 | 0.6348 | 0.6035 |
138
- | 0.2783 | 61.0 | 610 | 1.1536 | 0.6188 | 0.6360 | 0.6178 |
139
- | 0.2901 | 62.0 | 620 | 1.1183 | 0.6312 | 0.6412 | 0.6300 |
140
- | 0.3046 | 63.0 | 630 | 1.1705 | 0.6 | 0.6209 | 0.6026 |
141
- | 0.3066 | 64.0 | 640 | 1.1717 | 0.6375 | 0.6501 | 0.6328 |
142
- | 0.2978 | 65.0 | 650 | 1.1669 | 0.6375 | 0.6539 | 0.6332 |
143
- | 0.2967 | 66.0 | 660 | 1.2839 | 0.6188 | 0.6552 | 0.6097 |
144
- | 0.3624 | 67.0 | 670 | 1.2095 | 0.625 | 0.6622 | 0.6170 |
145
- | 0.2683 | 68.0 | 680 | 1.2292 | 0.6125 | 0.6504 | 0.6159 |
146
- | 0.2862 | 69.0 | 690 | 1.2228 | 0.6125 | 0.6252 | 0.6061 |
147
- | 0.252 | 70.0 | 700 | 1.4087 | 0.575 | 0.6327 | 0.5738 |
148
- | 0.2968 | 71.0 | 710 | 1.1559 | 0.6562 | 0.6769 | 0.6585 |
149
- | 0.247 | 72.0 | 720 | 1.1829 | 0.6062 | 0.6333 | 0.6108 |
150
- | 0.2849 | 73.0 | 730 | 1.2207 | 0.6312 | 0.6863 | 0.6321 |
151
- | 0.2684 | 74.0 | 740 | 1.1720 | 0.6625 | 0.6857 | 0.6658 |
152
- | 0.2649 | 75.0 | 750 | 1.2352 | 0.6375 | 0.6479 | 0.6359 |
153
- | 0.2265 | 76.0 | 760 | 1.2990 | 0.6 | 0.6427 | 0.6002 |
154
- | 0.2398 | 77.0 | 770 | 1.3163 | 0.6 | 0.6420 | 0.6007 |
155
- | 0.2324 | 78.0 | 780 | 1.3362 | 0.5938 | 0.5907 | 0.5730 |
156
- | 0.1927 | 79.0 | 790 | 1.2690 | 0.625 | 0.6552 | 0.6227 |
157
- | 0.1757 | 80.0 | 800 | 1.2791 | 0.65 | 0.6716 | 0.6487 |
158
- | 0.1993 | 81.0 | 810 | 1.2946 | 0.625 | 0.6564 | 0.6235 |
159
- | 0.2326 | 82.0 | 820 | 1.3964 | 0.5813 | 0.6042 | 0.5742 |
160
- | 0.2252 | 83.0 | 830 | 1.3020 | 0.6125 | 0.6567 | 0.6095 |
161
- | 0.228 | 84.0 | 840 | 1.2979 | 0.6312 | 0.6629 | 0.6358 |
162
- | 0.2055 | 85.0 | 850 | 1.2876 | 0.6125 | 0.6274 | 0.6086 |
163
- | 0.2171 | 86.0 | 860 | 1.2951 | 0.6312 | 0.6574 | 0.6308 |
164
- | 0.2156 | 87.0 | 870 | 1.3025 | 0.6 | 0.6072 | 0.5975 |
165
- | 0.1869 | 88.0 | 880 | 1.2232 | 0.6375 | 0.6822 | 0.6423 |
166
- | 0.2199 | 89.0 | 890 | 1.2538 | 0.6125 | 0.6056 | 0.6009 |
167
- | 0.189 | 90.0 | 900 | 1.3159 | 0.6188 | 0.6345 | 0.6198 |
168
- | 0.2023 | 91.0 | 910 | 1.3270 | 0.5938 | 0.6124 | 0.5910 |
169
- | 0.2304 | 92.0 | 920 | 1.2732 | 0.65 | 0.6642 | 0.6436 |
170
- | 0.2042 | 93.0 | 930 | 1.4199 | 0.55 | 0.5662 | 0.5401 |
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- | 0.1968 | 94.0 | 940 | 1.4262 | 0.5875 | 0.6388 | 0.5828 |
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- | 0.1968 | 95.0 | 950 | 1.3575 | 0.6062 | 0.6364 | 0.6090 |
173
- | 0.2176 | 96.0 | 960 | 1.3166 | 0.6062 | 0.6375 | 0.6080 |
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- | 0.1884 | 97.0 | 970 | 1.2959 | 0.5875 | 0.6066 | 0.5876 |
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- | 0.1841 | 98.0 | 980 | 1.4839 | 0.5875 | 0.6712 | 0.5838 |
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- | 0.2175 | 99.0 | 990 | 1.3247 | 0.6125 | 0.6385 | 0.6086 |
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- | 0.2091 | 100.0 | 1000 | 1.3601 | 0.6188 | 0.6490 | 0.6138 |
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- | 0.1656 | 101.0 | 1010 | 1.4244 | 0.6062 | 0.6495 | 0.6077 |
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- | 0.1897 | 102.0 | 1020 | 1.3256 | 0.6188 | 0.6774 | 0.6237 |
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- | 0.1816 | 103.0 | 1030 | 1.3440 | 0.6062 | 0.6390 | 0.6097 |
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- | 0.1973 | 104.0 | 1040 | 1.3377 | 0.625 | 0.6645 | 0.6240 |
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  ### Framework versions
 
24
  metrics:
25
  - name: Accuracy
26
  type: accuracy
27
+ value: 0.66875
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  - name: Precision
29
  type: precision
30
+ value: 0.684222027972028
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  - name: F1
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  type: f1
33
+ value: 0.6649370603045093
34
  ---
35
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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 image_folder dataset.
42
  It achieves the following results on the evaluation set:
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+ - Loss: 1.0254
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+ - Accuracy: 0.6687
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+ - Precision: 0.6842
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+ - F1: 0.6649
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
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+ | 2.079 | 1.0 | 10 | 2.0759 | 0.1437 | 0.1305 | 0.1297 |
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+ | 2.0798 | 2.0 | 20 | 2.0725 | 0.1688 | 0.1495 | 0.1503 |
80
+ | 2.0758 | 3.0 | 30 | 2.0668 | 0.2 | 0.1992 | 0.1859 |
81
+ | 2.0602 | 4.0 | 40 | 2.0595 | 0.225 | 0.2219 | 0.2100 |
82
+ | 2.0456 | 5.0 | 50 | 2.0495 | 0.225 | 0.2285 | 0.2105 |
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+ | 2.0303 | 6.0 | 60 | 2.0324 | 0.2437 | 0.2546 | 0.2267 |
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+ | 2.0009 | 7.0 | 70 | 1.9983 | 0.2437 | 0.2661 | 0.2291 |
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+ | 1.9482 | 8.0 | 80 | 1.9342 | 0.3375 | 0.3320 | 0.3183 |
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+ | 1.8709 | 9.0 | 90 | 1.8475 | 0.4 | 0.3524 | 0.3583 |
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+ | 1.7828 | 10.0 | 100 | 1.7259 | 0.4562 | 0.4074 | 0.4111 |
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+ | 1.6841 | 11.0 | 110 | 1.6324 | 0.4688 | 0.4211 | 0.4182 |
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+ | 1.6047 | 12.0 | 120 | 1.5508 | 0.4375 | 0.4049 | 0.3908 |
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+ | 1.5343 | 13.0 | 130 | 1.4942 | 0.5188 | 0.5115 | 0.4980 |
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+ | 1.4606 | 14.0 | 140 | 1.4133 | 0.55 | 0.5063 | 0.5083 |
92
+ | 1.3935 | 15.0 | 150 | 1.3513 | 0.5312 | 0.5377 | 0.5050 |
93
+ | 1.3695 | 16.0 | 160 | 1.2981 | 0.6062 | 0.6190 | 0.5899 |
94
+ | 1.2956 | 17.0 | 170 | 1.2630 | 0.5687 | 0.5654 | 0.5479 |
95
+ | 1.2481 | 18.0 | 180 | 1.2470 | 0.5875 | 0.5931 | 0.5735 |
96
+ | 1.2084 | 19.0 | 190 | 1.2095 | 0.5938 | 0.6143 | 0.5899 |
97
+ | 1.1676 | 20.0 | 200 | 1.1918 | 0.5938 | 0.6006 | 0.5788 |
98
+ | 1.0999 | 21.0 | 210 | 1.2066 | 0.5875 | 0.6020 | 0.5690 |
99
+ | 1.071 | 22.0 | 220 | 1.1474 | 0.6 | 0.5997 | 0.5852 |
100
+ | 0.9925 | 23.0 | 230 | 1.1266 | 0.6312 | 0.6504 | 0.6283 |
101
+ | 0.961 | 24.0 | 240 | 1.1031 | 0.5938 | 0.6021 | 0.5901 |
102
+ | 0.9364 | 25.0 | 250 | 1.1458 | 0.6 | 0.6199 | 0.5907 |
103
+ | 0.8906 | 26.0 | 260 | 1.1339 | 0.5875 | 0.6158 | 0.5789 |
104
+ | 0.882 | 27.0 | 270 | 1.0824 | 0.6312 | 0.6543 | 0.6303 |
105
+ | 0.827 | 28.0 | 280 | 1.1464 | 0.5875 | 0.6521 | 0.5793 |
106
+ | 0.7791 | 29.0 | 290 | 1.1309 | 0.575 | 0.5998 | 0.5566 |
107
+ | 0.7621 | 30.0 | 300 | 1.0579 | 0.6125 | 0.6277 | 0.6068 |
108
+ | 0.7245 | 31.0 | 310 | 1.0418 | 0.6562 | 0.6633 | 0.6533 |
109
+ | 0.6868 | 32.0 | 320 | 1.0555 | 0.6375 | 0.6470 | 0.6329 |
110
+ | 0.653 | 33.0 | 330 | 1.1451 | 0.5938 | 0.6330 | 0.5944 |
111
+ | 0.6102 | 34.0 | 340 | 1.0254 | 0.6687 | 0.6842 | 0.6649 |
112
+ | 0.5977 | 35.0 | 350 | 1.0981 | 0.625 | 0.6482 | 0.6227 |
113
+ | 0.6258 | 36.0 | 360 | 1.0975 | 0.6438 | 0.6773 | 0.6346 |
114
+ | 0.5444 | 37.0 | 370 | 1.1195 | 0.6125 | 0.6408 | 0.6147 |
115
+ | 0.5558 | 38.0 | 380 | 1.0637 | 0.625 | 0.6323 | 0.6201 |
116
+ | 0.5716 | 39.0 | 390 | 1.1407 | 0.6062 | 0.6463 | 0.6111 |
117
+ | 0.5048 | 40.0 | 400 | 1.1153 | 0.6312 | 0.6407 | 0.6244 |
118
+ | 0.4646 | 41.0 | 410 | 1.1072 | 0.625 | 0.6284 | 0.6225 |
119
+ | 0.463 | 42.0 | 420 | 1.1086 | 0.6062 | 0.6062 | 0.6026 |
120
+ | 0.4321 | 43.0 | 430 | 1.1725 | 0.6 | 0.6304 | 0.5960 |
121
+ | 0.49 | 44.0 | 440 | 1.1325 | 0.6188 | 0.6423 | 0.6166 |
122
+ | 0.408 | 45.0 | 450 | 1.2134 | 0.575 | 0.5865 | 0.5721 |
123
+ | 0.4296 | 46.0 | 460 | 1.2182 | 0.6188 | 0.6492 | 0.6175 |
124
+ | 0.3328 | 47.0 | 470 | 1.1789 | 0.6188 | 0.6378 | 0.6205 |
125
+ | 0.3781 | 48.0 | 480 | 1.2054 | 0.6125 | 0.6158 | 0.6077 |
126
+ | 0.3326 | 49.0 | 490 | 1.2308 | 0.5938 | 0.6148 | 0.5941 |
127
+ | 0.3526 | 50.0 | 500 | 1.2640 | 0.6 | 0.6038 | 0.5959 |
128
+ | 0.3967 | 51.0 | 510 | 1.3154 | 0.5437 | 0.5635 | 0.5410 |
129
+ | 0.4286 | 52.0 | 520 | 1.2358 | 0.6188 | 0.6488 | 0.6140 |
130
+ | 0.3411 | 53.0 | 530 | 1.1959 | 0.625 | 0.6368 | 0.6192 |
131
+ | 0.3455 | 54.0 | 540 | 1.2526 | 0.6 | 0.6168 | 0.5973 |
132
+ | 0.3224 | 55.0 | 550 | 1.1988 | 0.625 | 0.6490 | 0.6208 |
133
+ | 0.3015 | 56.0 | 560 | 1.2067 | 0.6062 | 0.6030 | 0.6005 |
134
+ | 0.322 | 57.0 | 570 | 1.2124 | 0.6188 | 0.6279 | 0.6181 |
135
+ | 0.2991 | 58.0 | 580 | 1.2274 | 0.6312 | 0.6368 | 0.6294 |
136
+ | 0.3199 | 59.0 | 590 | 1.2649 | 0.5938 | 0.5876 | 0.5880 |
137
+ | 0.3204 | 60.0 | 600 | 1.2636 | 0.6062 | 0.6239 | 0.6002 |
138
+ | 0.2831 | 61.0 | 610 | 1.3039 | 0.5875 | 0.5974 | 0.5832 |
139
+ | 0.2723 | 62.0 | 620 | 1.2620 | 0.625 | 0.6558 | 0.6236 |
140
+ | 0.2806 | 63.0 | 630 | 1.2368 | 0.6312 | 0.6364 | 0.6294 |
141
+ | 0.2621 | 64.0 | 640 | 1.2783 | 0.6062 | 0.6160 | 0.6049 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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