Hugging Face's logo

jayanta
/
microsoft-resnet-50-cartoon-emotion-detection

Image Classification
Transformers
PyTorch
TensorBoard
resnet
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
15
paul commited on
Commit
5f318a0
•
1 Parent(s): 351d245

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +94 -34
README.md CHANGED
@@ -24,16 +24,16 @@ model-index:
24
  metrics:
25
  - name: Accuracy
26
  type: accuracy
27
- value: 0.6697247706422018
28
  - name: Precision
29
  type: precision
30
- value: 0.5798801171844885
31
  - name: Recall
32
  type: recall
33
- value: 0.6697247706422018
34
  - name: F1
35
  type: f1
36
- value: 0.6086361803243947
37
  ---
38
 
39
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
43
 
44
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
45
  It achieves the following results on the evaluation set:
46
- - Loss: 1.0059
47
- - Accuracy: 0.6697
48
- - Precision: 0.5799
49
- - Recall: 0.6697
50
- - F1: 0.6086
51
 
52
  ## Model description
53
 
@@ -75,37 +75,97 @@ The following hyperparameters were used during training:
75
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
76
  - lr_scheduler_type: linear
77
  - lr_scheduler_warmup_ratio: 0.1
78
- - num_epochs: 20
79
 
80
  ### Training results
81
 
82
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
83
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
84
- | No log | 0.97 | 8 | 1.3833 | 0.2477 | 0.2054 | 0.2477 | 0.2042 |
85
- | 1.4276 | 1.97 | 16 | 1.3711 | 0.3028 | 0.1982 | 0.3028 | 0.1932 |
86
- | 1.4046 | 2.97 | 24 | 1.3550 | 0.3028 | 0.0917 | 0.3028 | 0.1407 |
87
- | 1.3817 | 3.97 | 32 | 1.3375 | 0.3119 | 0.2852 | 0.3119 | 0.1592 |
88
- | 1.3562 | 4.97 | 40 | 1.3179 | 0.3211 | 0.4337 | 0.3211 | 0.1785 |
89
- | 1.3562 | 5.97 | 48 | 1.2991 | 0.3761 | 0.5442 | 0.3761 | 0.2741 |
90
- | 1.3624 | 6.97 | 56 | 1.2751 | 0.4495 | 0.5593 | 0.4495 | 0.3659 |
91
- | 1.2914 | 7.97 | 64 | 1.2494 | 0.4771 | 0.5442 | 0.4771 | 0.4094 |
92
- | 1.2518 | 8.97 | 72 | 1.2279 | 0.5046 | 0.5525 | 0.5046 | 0.4430 |
93
- | 1.2085 | 9.97 | 80 | 1.1905 | 0.5321 | 0.5134 | 0.5321 | 0.4579 |
94
- | 1.2085 | 10.97 | 88 | 1.1602 | 0.5505 | 0.5151 | 0.5505 | 0.4872 |
95
- | 1.1865 | 11.97 | 96 | 1.1307 | 0.5963 | 0.5969 | 0.5963 | 0.5416 |
96
- | 1.122 | 12.97 | 104 | 1.1037 | 0.5872 | 0.5069 | 0.5872 | 0.5206 |
97
- | 1.0812 | 13.97 | 112 | 1.0797 | 0.5688 | 0.4868 | 0.5688 | 0.5068 |
98
- | 1.0449 | 14.97 | 120 | 1.0712 | 0.6239 | 0.5269 | 0.6239 | 0.5641 |
99
- | 1.0449 | 15.97 | 128 | 1.0425 | 0.6239 | 0.5123 | 0.6239 | 0.5517 |
100
- | 1.0458 | 16.97 | 136 | 1.0346 | 0.6239 | 0.6487 | 0.6239 | 0.5782 |
101
- | 1.004 | 17.97 | 144 | 1.0264 | 0.6330 | 0.5472 | 0.6330 | 0.5721 |
102
- | 0.9806 | 18.97 | 152 | 1.0041 | 0.6606 | 0.6334 | 0.6606 | 0.6069 |
103
- | 0.97 | 19.97 | 160 | 1.0059 | 0.6697 | 0.5799 | 0.6697 | 0.6086 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
104
 
105
 
106
  ### Framework versions
107
 
108
- - Transformers 4.24.0.dev0
109
- - Pytorch 1.11.0+cu102
110
- - Datasets 2.6.1
111
- - Tokenizers 0.13.1
 
24
  metrics:
25
  - name: Accuracy
26
  type: accuracy
27
+ value: 0.8165137614678899
28
  - name: Precision
29
  type: precision
30
+ value: 0.8181998512273742
31
  - name: Recall
32
  type: recall
33
+ value: 0.8165137614678899
34
  - name: F1
35
  type: f1
36
+ value: 0.8172526992448356
37
  ---
38
 
39
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
43
 
44
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
45
  It achieves the following results on the evaluation set:
46
+ - Loss: 0.4801
47
+ - Accuracy: 0.8165
48
+ - Precision: 0.8182
49
+ - Recall: 0.8165
50
+ - F1: 0.8173
51
 
52
  ## Model description
53
 
 
75
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
76
  - lr_scheduler_type: linear
77
  - lr_scheduler_warmup_ratio: 0.1
78
+ - num_epochs: 80
79
 
80
  ### Training results
81
 
82
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
83
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
84
+ | No log | 0.97 | 8 | 1.3855 | 0.2294 | 0.2697 | 0.2294 | 0.2165 |
85
+ | 1.4222 | 1.97 | 16 | 1.3792 | 0.2569 | 0.2808 | 0.2569 | 0.2543 |
86
+ | 1.4183 | 2.97 | 24 | 1.3646 | 0.3853 | 0.4102 | 0.3853 | 0.3511 |
87
+ | 1.4097 | 3.97 | 32 | 1.3563 | 0.4128 | 0.5062 | 0.4128 | 0.3245 |
88
+ | 1.3944 | 4.97 | 40 | 1.3462 | 0.4037 | 0.3927 | 0.4037 | 0.2939 |
89
+ | 1.3944 | 5.97 | 48 | 1.3223 | 0.4037 | 0.5152 | 0.4037 | 0.2841 |
90
+ | 1.411 | 6.97 | 56 | 1.3040 | 0.4128 | 0.4404 | 0.4128 | 0.2985 |
91
+ | 1.346 | 7.97 | 64 | 1.2700 | 0.4954 | 0.4960 | 0.4954 | 0.4093 |
92
+ | 1.3031 | 8.97 | 72 | 1.2150 | 0.5596 | 0.5440 | 0.5596 | 0.4672 |
93
+ | 1.2371 | 9.97 | 80 | 1.1580 | 0.5963 | 0.5659 | 0.5963 | 0.5101 |
94
+ | 1.2371 | 10.97 | 88 | 1.0670 | 0.6055 | 0.7279 | 0.6055 | 0.5211 |
95
+ | 1.1736 | 11.97 | 96 | 0.9856 | 0.6606 | 0.5537 | 0.6606 | 0.5772 |
96
+ | 1.0457 | 12.97 | 104 | 0.8963 | 0.6697 | 0.7631 | 0.6697 | 0.5965 |
97
+ | 0.953 | 13.97 | 112 | 0.8547 | 0.6697 | 0.6885 | 0.6697 | 0.6081 |
98
+ | 0.8579 | 14.97 | 120 | 0.7849 | 0.7156 | 0.7396 | 0.7156 | 0.6643 |
99
+ | 0.8579 | 15.97 | 128 | 0.7564 | 0.7431 | 0.7372 | 0.7431 | 0.7119 |
100
+ | 0.8167 | 16.97 | 136 | 0.7133 | 0.7615 | 0.7507 | 0.7615 | 0.7211 |
101
+ | 0.7273 | 17.97 | 144 | 0.6888 | 0.7523 | 0.7379 | 0.7523 | 0.7202 |
102
+ | 0.6547 | 18.97 | 152 | 0.6592 | 0.7798 | 0.7773 | 0.7798 | 0.7577 |
103
+ | 0.5963 | 19.97 | 160 | 0.6136 | 0.7706 | 0.7642 | 0.7706 | 0.7551 |
104
+ | 0.5963 | 20.97 | 168 | 0.5723 | 0.7890 | 0.7802 | 0.7890 | 0.7787 |
105
+ | 0.551 | 21.97 | 176 | 0.5686 | 0.7890 | 0.7761 | 0.7890 | 0.7781 |
106
+ | 0.4929 | 22.97 | 184 | 0.5597 | 0.7706 | 0.7649 | 0.7706 | 0.7651 |
107
+ | 0.4309 | 23.97 | 192 | 0.5234 | 0.7890 | 0.7774 | 0.7890 | 0.7810 |
108
+ | 0.3945 | 24.97 | 200 | 0.5008 | 0.7890 | 0.7837 | 0.7890 | 0.7813 |
109
+ | 0.3945 | 25.97 | 208 | 0.5289 | 0.7523 | 0.7537 | 0.7523 | 0.7529 |
110
+ | 0.3704 | 26.97 | 216 | 0.4399 | 0.7982 | 0.7958 | 0.7982 | 0.7963 |
111
+ | 0.3267 | 27.97 | 224 | 0.4539 | 0.8073 | 0.7983 | 0.8073 | 0.8005 |
112
+ | 0.2966 | 28.97 | 232 | 0.4735 | 0.7798 | 0.7892 | 0.7798 | 0.7837 |
113
+ | 0.2645 | 29.97 | 240 | 0.4594 | 0.7706 | 0.7706 | 0.7706 | 0.7706 |
114
+ | 0.2645 | 30.97 | 248 | 0.4699 | 0.7523 | 0.7554 | 0.7523 | 0.7533 |
115
+ | 0.2527 | 31.97 | 256 | 0.4551 | 0.7890 | 0.7856 | 0.7890 | 0.7857 |
116
+ | 0.2202 | 32.97 | 264 | 0.4458 | 0.8165 | 0.8198 | 0.8165 | 0.8170 |
117
+ | 0.2006 | 33.97 | 272 | 0.4632 | 0.7798 | 0.7941 | 0.7798 | 0.7850 |
118
+ | 0.1589 | 34.97 | 280 | 0.4651 | 0.7890 | 0.7993 | 0.7890 | 0.7925 |
119
+ | 0.1589 | 35.97 | 288 | 0.4595 | 0.7798 | 0.7824 | 0.7798 | 0.7804 |
120
+ | 0.153 | 36.97 | 296 | 0.4584 | 0.7615 | 0.7691 | 0.7615 | 0.7633 |
121
+ | 0.1427 | 37.97 | 304 | 0.4608 | 0.7798 | 0.7830 | 0.7798 | 0.7796 |
122
+ | 0.113 | 38.97 | 312 | 0.4571 | 0.7890 | 0.7922 | 0.7890 | 0.7899 |
123
+ | 0.1146 | 39.97 | 320 | 0.5270 | 0.7615 | 0.7651 | 0.7615 | 0.7613 |
124
+ | 0.1146 | 40.97 | 328 | 0.4888 | 0.7706 | 0.7782 | 0.7706 | 0.7710 |
125
+ | 0.1275 | 41.97 | 336 | 0.4523 | 0.7890 | 0.7809 | 0.7890 | 0.7837 |
126
+ | 0.0959 | 42.97 | 344 | 0.4697 | 0.7798 | 0.7753 | 0.7798 | 0.7767 |
127
+ | 0.0882 | 43.97 | 352 | 0.4286 | 0.7706 | 0.7686 | 0.7706 | 0.7686 |
128
+ | 0.0847 | 44.97 | 360 | 0.5317 | 0.7890 | 0.7993 | 0.7890 | 0.7925 |
129
+ | 0.0847 | 45.97 | 368 | 0.5431 | 0.7615 | 0.7700 | 0.7615 | 0.7647 |
130
+ | 0.0813 | 46.97 | 376 | 0.4432 | 0.8257 | 0.8435 | 0.8257 | 0.8284 |
131
+ | 0.0768 | 47.97 | 384 | 0.4886 | 0.7982 | 0.8005 | 0.7982 | 0.7956 |
132
+ | 0.0627 | 48.97 | 392 | 0.5373 | 0.7982 | 0.8072 | 0.7982 | 0.8010 |
133
+ | 0.0688 | 49.97 | 400 | 0.5897 | 0.7798 | 0.7892 | 0.7798 | 0.7822 |
134
+ | 0.0688 | 50.97 | 408 | 0.5115 | 0.7982 | 0.8015 | 0.7982 | 0.7992 |
135
+ | 0.0676 | 51.97 | 416 | 0.4881 | 0.7982 | 0.7998 | 0.7982 | 0.7978 |
136
+ | 0.0539 | 52.97 | 424 | 0.4820 | 0.8073 | 0.8139 | 0.8073 | 0.8077 |
137
+ | 0.0596 | 53.97 | 432 | 0.4450 | 0.8257 | 0.8246 | 0.8257 | 0.8244 |
138
+ | 0.0611 | 54.97 | 440 | 0.5057 | 0.7890 | 0.8008 | 0.7890 | 0.7924 |
139
+ | 0.0611 | 55.97 | 448 | 0.4918 | 0.7982 | 0.8056 | 0.7982 | 0.8008 |
140
+ | 0.0643 | 56.97 | 456 | 0.5946 | 0.7523 | 0.7587 | 0.7523 | 0.7545 |
141
+ | 0.0605 | 57.97 | 464 | 0.4888 | 0.8073 | 0.8239 | 0.8073 | 0.8121 |
142
+ | 0.063 | 58.97 | 472 | 0.5917 | 0.7890 | 0.8051 | 0.7890 | 0.7937 |
143
+ | 0.0595 | 59.97 | 480 | 0.5117 | 0.7890 | 0.7904 | 0.7890 | 0.7894 |
144
+ | 0.0595 | 60.97 | 488 | 0.5497 | 0.7615 | 0.7692 | 0.7615 | 0.7635 |
145
+ | 0.0554 | 61.97 | 496 | 0.4742 | 0.8165 | 0.8101 | 0.8165 | 0.8126 |
146
+ | 0.0557 | 62.97 | 504 | 0.5369 | 0.7890 | 0.7886 | 0.7890 | 0.7886 |
147
+ | 0.0539 | 63.97 | 512 | 0.5440 | 0.7890 | 0.7922 | 0.7890 | 0.7899 |
148
+ | 0.048 | 64.97 | 520 | 0.5924 | 0.7890 | 0.7878 | 0.7890 | 0.7883 |
149
+ | 0.048 | 65.97 | 528 | 0.4863 | 0.8440 | 0.8440 | 0.8440 | 0.8440 |
150
+ | 0.045 | 66.97 | 536 | 0.5850 | 0.8073 | 0.8076 | 0.8073 | 0.8047 |
151
+ | 0.047 | 67.97 | 544 | 0.4939 | 0.8257 | 0.8212 | 0.8257 | 0.8227 |
152
+ | 0.0412 | 68.97 | 552 | 0.4850 | 0.7890 | 0.7912 | 0.7890 | 0.7900 |
153
+ | 0.0392 | 69.97 | 560 | 0.5066 | 0.8257 | 0.8265 | 0.8257 | 0.8258 |
154
+ | 0.0392 | 70.97 | 568 | 0.4965 | 0.8073 | 0.8053 | 0.8073 | 0.8058 |
155
+ | 0.0423 | 71.97 | 576 | 0.4717 | 0.8349 | 0.8376 | 0.8349 | 0.8351 |
156
+ | 0.0471 | 72.97 | 584 | 0.4845 | 0.8257 | 0.8378 | 0.8257 | 0.8296 |
157
+ | 0.0322 | 73.97 | 592 | 0.5188 | 0.7706 | 0.7689 | 0.7706 | 0.7693 |
158
+ | 0.042 | 74.97 | 600 | 0.5242 | 0.7706 | 0.7699 | 0.7706 | 0.7701 |
159
+ | 0.042 | 75.97 | 608 | 0.5945 | 0.7798 | 0.7824 | 0.7798 | 0.7804 |
160
+ | 0.0416 | 76.97 | 616 | 0.5432 | 0.7982 | 0.8038 | 0.7982 | 0.7993 |
161
+ | 0.0399 | 77.97 | 624 | 0.5381 | 0.7982 | 0.8072 | 0.7982 | 0.7994 |
162
+ | 0.0439 | 78.97 | 632 | 0.6181 | 0.7798 | 0.7878 | 0.7798 | 0.7827 |
163
+ | 0.0462 | 79.97 | 640 | 0.4801 | 0.8165 | 0.8182 | 0.8165 | 0.8173 |
164
 
165
 
166
  ### Framework versions
167
 
168
+ - Transformers 4.25.1
169
+ - Pytorch 1.13.1+cu117
170
+ - Datasets 2.8.0
171
+ - Tokenizers 0.11.0