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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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End of training

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  1. README.md +120 -75
  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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- - imagefolder
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  metrics:
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  - accuracy
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  - precision
@@ -16,21 +16,21 @@ 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: imagefolder
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- type: imagefolder
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- config: default
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  split: train
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- args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.64375
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  - name: Precision
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  type: precision
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- value: 0.650616883116883
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  - name: F1
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  type: f1
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- value: 0.6344950707077283
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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
@@ -38,12 +38,12 @@ should probably proofread and complete it, then remove this comment. -->
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  # emotion_classification
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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: 1.1553
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- - Accuracy: 0.6438
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- - Precision: 0.6506
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- - F1: 0.6345
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48
  ## Model description
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@@ -62,7 +62,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
@@ -75,70 +75,115 @@ 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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- | 2.0799 | 1.0 | 10 | 2.0707 | 0.1313 | 0.1740 | 0.1156 |
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- | 2.0811 | 2.0 | 20 | 2.0681 | 0.1437 | 0.1617 | 0.1245 |
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- | 2.0709 | 3.0 | 30 | 2.0640 | 0.1562 | 0.1544 | 0.1330 |
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- | 2.0701 | 4.0 | 40 | 2.0590 | 0.1688 | 0.1463 | 0.1431 |
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- | 2.0639 | 5.0 | 50 | 2.0529 | 0.1812 | 0.1676 | 0.1613 |
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- | 2.0499 | 6.0 | 60 | 2.0439 | 0.2 | 0.2050 | 0.1871 |
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- | 2.0387 | 7.0 | 70 | 2.0322 | 0.25 | 0.2679 | 0.2373 |
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- | 2.0235 | 8.0 | 80 | 2.0141 | 0.3312 | 0.3638 | 0.3331 |
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- | 1.9933 | 9.0 | 90 | 1.9883 | 0.3375 | 0.3752 | 0.3392 |
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- | 1.9573 | 10.0 | 100 | 1.9473 | 0.3563 | 0.3940 | 0.3535 |
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- | 1.912 | 11.0 | 110 | 1.8863 | 0.3875 | 0.4352 | 0.3759 |
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- | 1.8306 | 12.0 | 120 | 1.8102 | 0.3875 | 0.4062 | 0.3586 |
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- | 1.7479 | 13.0 | 130 | 1.7158 | 0.4062 | 0.4056 | 0.3689 |
91
- | 1.665 | 14.0 | 140 | 1.6250 | 0.475 | 0.4543 | 0.4248 |
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- | 1.6115 | 15.0 | 150 | 1.5597 | 0.4875 | 0.4646 | 0.4414 |
93
- | 1.5716 | 16.0 | 160 | 1.5112 | 0.5125 | 0.4846 | 0.4575 |
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- | 1.5062 | 17.0 | 170 | 1.4672 | 0.525 | 0.4932 | 0.4925 |
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- | 1.4655 | 18.0 | 180 | 1.4262 | 0.5312 | 0.5018 | 0.4876 |
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- | 1.413 | 19.0 | 190 | 1.3851 | 0.575 | 0.5253 | 0.5317 |
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- | 1.3758 | 20.0 | 200 | 1.3421 | 0.5625 | 0.5900 | 0.5113 |
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- | 1.317 | 21.0 | 210 | 1.3156 | 0.55 | 0.5835 | 0.4996 |
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- | 1.291 | 22.0 | 220 | 1.2712 | 0.5938 | 0.6374 | 0.5601 |
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- | 1.2369 | 23.0 | 230 | 1.2697 | 0.5563 | 0.5681 | 0.5250 |
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- | 1.2139 | 24.0 | 240 | 1.2439 | 0.5625 | 0.5733 | 0.5417 |
102
- | 1.1766 | 25.0 | 250 | 1.2228 | 0.5938 | 0.6099 | 0.5735 |
103
- | 1.1483 | 26.0 | 260 | 1.2464 | 0.5625 | 0.6016 | 0.5508 |
104
- | 1.1344 | 27.0 | 270 | 1.1877 | 0.5875 | 0.6142 | 0.5718 |
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- | 1.0898 | 28.0 | 280 | 1.1871 | 0.6 | 0.6481 | 0.5817 |
106
- | 1.0515 | 29.0 | 290 | 1.1553 | 0.6438 | 0.6506 | 0.6345 |
107
- | 1.0628 | 30.0 | 300 | 1.1603 | 0.575 | 0.6209 | 0.5727 |
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- | 1.0257 | 31.0 | 310 | 1.1326 | 0.6125 | 0.6312 | 0.6109 |
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- | 1.0048 | 32.0 | 320 | 1.1450 | 0.6125 | 0.6402 | 0.6079 |
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- | 0.9646 | 33.0 | 330 | 1.1250 | 0.6062 | 0.6161 | 0.6004 |
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- | 0.9231 | 34.0 | 340 | 1.1299 | 0.6 | 0.6183 | 0.5976 |
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- | 0.8944 | 35.0 | 350 | 1.1312 | 0.5938 | 0.5996 | 0.5885 |
113
- | 0.9001 | 36.0 | 360 | 1.1293 | 0.625 | 0.6358 | 0.6220 |
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- | 0.8587 | 37.0 | 370 | 1.1415 | 0.6062 | 0.6122 | 0.6037 |
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- | 0.8708 | 38.0 | 380 | 1.1171 | 0.6062 | 0.6379 | 0.5985 |
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- | 0.843 | 39.0 | 390 | 1.1220 | 0.625 | 0.6658 | 0.6229 |
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- | 0.8038 | 40.0 | 400 | 1.1144 | 0.6188 | 0.6243 | 0.6153 |
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- | 0.7815 | 41.0 | 410 | 1.1538 | 0.575 | 0.6042 | 0.5679 |
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- | 0.7289 | 42.0 | 420 | 1.1125 | 0.6062 | 0.6218 | 0.6024 |
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- | 0.7255 | 43.0 | 430 | 1.1401 | 0.6 | 0.6307 | 0.5947 |
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- | 0.7182 | 44.0 | 440 | 1.1092 | 0.6 | 0.6121 | 0.5916 |
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- | 0.6533 | 45.0 | 450 | 1.1219 | 0.625 | 0.6448 | 0.6268 |
123
- | 0.6658 | 46.0 | 460 | 1.1322 | 0.6125 | 0.6272 | 0.6135 |
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- | 0.6293 | 47.0 | 470 | 1.1306 | 0.6 | 0.6075 | 0.5980 |
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- | 0.6287 | 48.0 | 480 | 1.1227 | 0.6125 | 0.6210 | 0.6099 |
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- | 0.622 | 49.0 | 490 | 1.1441 | 0.5938 | 0.6154 | 0.5940 |
127
- | 0.6004 | 50.0 | 500 | 1.1119 | 0.625 | 0.6267 | 0.6206 |
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- | 0.606 | 51.0 | 510 | 1.1301 | 0.5938 | 0.6146 | 0.5925 |
129
- | 0.5924 | 52.0 | 520 | 1.1552 | 0.6062 | 0.6135 | 0.6022 |
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- | 0.5639 | 53.0 | 530 | 1.1956 | 0.5938 | 0.6411 | 0.5945 |
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- | 0.5434 | 54.0 | 540 | 1.1843 | 0.5813 | 0.5925 | 0.5765 |
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- | 0.5479 | 55.0 | 550 | 1.1529 | 0.6125 | 0.6247 | 0.6142 |
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- | 0.5227 | 56.0 | 560 | 1.1730 | 0.5687 | 0.5724 | 0.5628 |
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- | 0.5402 | 57.0 | 570 | 1.1919 | 0.6 | 0.6075 | 0.5954 |
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- | 0.4971 | 58.0 | 580 | 1.1761 | 0.5938 | 0.5984 | 0.5925 |
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- | 0.5004 | 59.0 | 590 | 1.2305 | 0.5687 | 0.5957 | 0.5645 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
137
 
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139
  ### Framework versions
140
 
141
- - Transformers 4.33.1
142
  - Pytorch 2.0.0
143
- - Datasets 2.14.5
144
  - Tokenizers 0.13.3
 
4
  tags:
5
  - generated_from_trainer
6
  datasets:
7
+ - image_folder
8
  metrics:
9
  - accuracy
10
  - precision
 
16
  name: Image Classification
17
  type: image-classification
18
  dataset:
19
+ name: image_folder
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+ type: image_folder
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+ config: FastJobs--Visual_Emotional_Analysis
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  split: train
23
+ args: FastJobs--Visual_Emotional_Analysis
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  metrics:
25
  - name: Accuracy
26
  type: accuracy
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+ value: 0.6625
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  - name: Precision
29
  type: precision
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+ value: 0.6857332900074835
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  - name: F1
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  type: f1
33
+ value: 0.6658368805611075
34
  ---
35
 
36
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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 image_folder dataset.
42
  It achieves the following results on the evaluation set:
43
+ - Loss: 1.1720
44
+ - Accuracy: 0.6625
45
+ - Precision: 0.6857
46
+ - F1: 0.6658
47
 
48
  ## Model description
49
 
 
62
  ### Training hyperparameters
63
 
64
  The following hyperparameters were used during training:
65
+ - learning_rate: 5e-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
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
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+ | 2.0805 | 1.0 | 10 | 2.0844 | 0.1688 | 0.1731 | 0.1670 |
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+ | 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 |
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+ | 2.0653 | 4.0 | 40 | 2.0663 | 0.2062 | 0.1832 | 0.1893 |
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+ | 2.0586 | 5.0 | 50 | 2.0547 | 0.2062 | 0.1817 | 0.1911 |
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+ | 2.0347 | 6.0 | 60 | 2.0343 | 0.2375 | 0.2074 | 0.2187 |
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+ | 2.009 | 7.0 | 70 | 2.0039 | 0.2875 | 0.4007 | 0.2750 |
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+ | 1.9672 | 8.0 | 80 | 1.9560 | 0.3187 | 0.3615 | 0.3006 |
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+ | 1.9015 | 9.0 | 90 | 1.8650 | 0.3688 | 0.4229 | 0.3577 |
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+ | 1.812 | 10.0 | 100 | 1.7339 | 0.4375 | 0.3925 | 0.4045 |
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+ | 1.6993 | 11.0 | 110 | 1.6196 | 0.4688 | 0.4093 | 0.4267 |
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+ | 1.6037 | 12.0 | 120 | 1.5466 | 0.475 | 0.4808 | 0.4413 |
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+ | 1.5332 | 13.0 | 130 | 1.4791 | 0.525 | 0.4749 | 0.4922 |
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+ | 1.4649 | 14.0 | 140 | 1.4201 | 0.525 | 0.4860 | 0.4948 |
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+ | 1.4142 | 15.0 | 150 | 1.3659 | 0.55 | 0.5231 | 0.5178 |
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+ | 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 |
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+ | 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 |
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+ | 1.0719 | 22.0 | 220 | 1.1473 | 0.5875 | 0.5899 | 0.5721 |
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+ | 0.9894 | 23.0 | 230 | 1.1303 | 0.6125 | 0.6507 | 0.6124 |
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+ | 0.9698 | 24.0 | 240 | 1.1010 | 0.6188 | 0.6323 | 0.6142 |
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+ | 0.9081 | 25.0 | 250 | 1.1038 | 0.5938 | 0.6074 | 0.5923 |
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+ | 0.8739 | 26.0 | 260 | 1.1383 | 0.5563 | 0.5874 | 0.5447 |
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+ | 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 |
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+ | 0.7614 | 29.0 | 290 | 1.1002 | 0.6188 | 0.6724 | 0.6089 |
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+ | 0.7937 | 30.0 | 300 | 1.0272 | 0.6188 | 0.6515 | 0.6135 |
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+ | 0.7379 | 31.0 | 310 | 1.0184 | 0.6062 | 0.6120 | 0.6035 |
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+ | 0.6994 | 32.0 | 320 | 1.0163 | 0.5875 | 0.5966 | 0.5772 |
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+ | 0.684 | 33.0 | 330 | 1.0420 | 0.6312 | 0.6627 | 0.6327 |
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+ | 0.605 | 34.0 | 340 | 1.0555 | 0.6312 | 0.6822 | 0.6353 |
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+ | 0.5851 | 35.0 | 350 | 1.0991 | 0.625 | 0.6941 | 0.6269 |
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+ | 0.6186 | 36.0 | 360 | 1.1196 | 0.6188 | 0.6916 | 0.6077 |
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+ | 0.5349 | 37.0 | 370 | 1.0707 | 0.6062 | 0.6123 | 0.5978 |
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+ | 0.5549 | 38.0 | 380 | 1.0161 | 0.6375 | 0.6498 | 0.6308 |
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+ | 0.577 | 39.0 | 390 | 1.1375 | 0.5813 | 0.6449 | 0.5770 |
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+ | 0.5151 | 40.0 | 400 | 1.0479 | 0.65 | 0.6691 | 0.6421 |
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+ | 0.4898 | 41.0 | 410 | 1.0835 | 0.6125 | 0.6378 | 0.6106 |
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+ | 0.4619 | 42.0 | 420 | 1.0262 | 0.6375 | 0.6596 | 0.6418 |
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+ | 0.4142 | 43.0 | 430 | 1.1238 | 0.6188 | 0.6422 | 0.6143 |
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+ | 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 |
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+ | 0.4144 | 46.0 | 460 | 1.1255 | 0.6 | 0.6308 | 0.6023 |
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+ | 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 |
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+ | 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 |
128
+ | 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 |
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+ | 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 |
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+ | 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 |
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+ | 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 |
171
+ | 0.1968 | 94.0 | 940 | 1.4262 | 0.5875 | 0.6388 | 0.5828 |
172
+ | 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 |
174
+ | 0.1884 | 97.0 | 970 | 1.2959 | 0.5875 | 0.6066 | 0.5876 |
175
+ | 0.1841 | 98.0 | 980 | 1.4839 | 0.5875 | 0.6712 | 0.5838 |
176
+ | 0.2175 | 99.0 | 990 | 1.3247 | 0.6125 | 0.6385 | 0.6086 |
177
+ | 0.2091 | 100.0 | 1000 | 1.3601 | 0.6188 | 0.6490 | 0.6138 |
178
+ | 0.1656 | 101.0 | 1010 | 1.4244 | 0.6062 | 0.6495 | 0.6077 |
179
+ | 0.1897 | 102.0 | 1020 | 1.3256 | 0.6188 | 0.6774 | 0.6237 |
180
+ | 0.1816 | 103.0 | 1030 | 1.3440 | 0.6062 | 0.6390 | 0.6097 |
181
+ | 0.1973 | 104.0 | 1040 | 1.3377 | 0.625 | 0.6645 | 0.6240 |
182
 
183
 
184
  ### Framework versions
185
 
186
+ - Transformers 4.33.0
187
  - Pytorch 2.0.0
188
+ - Datasets 2.1.0
189
  - 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": "4.33.1"
44
  }
 
40
  "problem_type": "single_label_classification",
41
  "qkv_bias": true,
42
  "torch_dtype": "float32",
43
+ "transformers_version": "4.33.0"
44
  }
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
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