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# microsoft-resnet-50-cartoon-emotion-detection

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4801
- Accuracy: 0.8165
- Precision: 0.8182
- Recall: 0.8165
- F1: 0.8173

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.97  | 8    | 1.3855          | 0.2294   | 0.2697    | 0.2294 | 0.2165 |
| 1.4222        | 1.97  | 16   | 1.3792          | 0.2569   | 0.2808    | 0.2569 | 0.2543 |
| 1.4183        | 2.97  | 24   | 1.3646          | 0.3853   | 0.4102    | 0.3853 | 0.3511 |
| 1.4097        | 3.97  | 32   | 1.3563          | 0.4128   | 0.5062    | 0.4128 | 0.3245 |
| 1.3944        | 4.97  | 40   | 1.3462          | 0.4037   | 0.3927    | 0.4037 | 0.2939 |
| 1.3944        | 5.97  | 48   | 1.3223          | 0.4037   | 0.5152    | 0.4037 | 0.2841 |
| 1.411         | 6.97  | 56   | 1.3040          | 0.4128   | 0.4404    | 0.4128 | 0.2985 |
| 1.346         | 7.97  | 64   | 1.2700          | 0.4954   | 0.4960    | 0.4954 | 0.4093 |
| 1.3031        | 8.97  | 72   | 1.2150          | 0.5596   | 0.5440    | 0.5596 | 0.4672 |
| 1.2371        | 9.97  | 80   | 1.1580          | 0.5963   | 0.5659    | 0.5963 | 0.5101 |
| 1.2371        | 10.97 | 88   | 1.0670          | 0.6055   | 0.7279    | 0.6055 | 0.5211 |
| 1.1736        | 11.97 | 96   | 0.9856          | 0.6606   | 0.5537    | 0.6606 | 0.5772 |
| 1.0457        | 12.97 | 104  | 0.8963          | 0.6697   | 0.7631    | 0.6697 | 0.5965 |
| 0.953         | 13.97 | 112  | 0.8547          | 0.6697   | 0.6885    | 0.6697 | 0.6081 |
| 0.8579        | 14.97 | 120  | 0.7849          | 0.7156   | 0.7396    | 0.7156 | 0.6643 |
| 0.8579        | 15.97 | 128  | 0.7564          | 0.7431   | 0.7372    | 0.7431 | 0.7119 |
| 0.8167        | 16.97 | 136  | 0.7133          | 0.7615   | 0.7507    | 0.7615 | 0.7211 |
| 0.7273        | 17.97 | 144  | 0.6888          | 0.7523   | 0.7379    | 0.7523 | 0.7202 |
| 0.6547        | 18.97 | 152  | 0.6592          | 0.7798   | 0.7773    | 0.7798 | 0.7577 |
| 0.5963        | 19.97 | 160  | 0.6136          | 0.7706   | 0.7642    | 0.7706 | 0.7551 |
| 0.5963        | 20.97 | 168  | 0.5723          | 0.7890   | 0.7802    | 0.7890 | 0.7787 |
| 0.551         | 21.97 | 176  | 0.5686          | 0.7890   | 0.7761    | 0.7890 | 0.7781 |
| 0.4929        | 22.97 | 184  | 0.5597          | 0.7706   | 0.7649    | 0.7706 | 0.7651 |
| 0.4309        | 23.97 | 192  | 0.5234          | 0.7890   | 0.7774    | 0.7890 | 0.7810 |
| 0.3945        | 24.97 | 200  | 0.5008          | 0.7890   | 0.7837    | 0.7890 | 0.7813 |
| 0.3945        | 25.97 | 208  | 0.5289          | 0.7523   | 0.7537    | 0.7523 | 0.7529 |
| 0.3704        | 26.97 | 216  | 0.4399          | 0.7982   | 0.7958    | 0.7982 | 0.7963 |
| 0.3267        | 27.97 | 224  | 0.4539          | 0.8073   | 0.7983    | 0.8073 | 0.8005 |
| 0.2966        | 28.97 | 232  | 0.4735          | 0.7798   | 0.7892    | 0.7798 | 0.7837 |
| 0.2645        | 29.97 | 240  | 0.4594          | 0.7706   | 0.7706    | 0.7706 | 0.7706 |
| 0.2645        | 30.97 | 248  | 0.4699          | 0.7523   | 0.7554    | 0.7523 | 0.7533 |
| 0.2527        | 31.97 | 256  | 0.4551          | 0.7890   | 0.7856    | 0.7890 | 0.7857 |
| 0.2202        | 32.97 | 264  | 0.4458          | 0.8165   | 0.8198    | 0.8165 | 0.8170 |
| 0.2006        | 33.97 | 272  | 0.4632          | 0.7798   | 0.7941    | 0.7798 | 0.7850 |
| 0.1589        | 34.97 | 280  | 0.4651          | 0.7890   | 0.7993    | 0.7890 | 0.7925 |
| 0.1589        | 35.97 | 288  | 0.4595          | 0.7798   | 0.7824    | 0.7798 | 0.7804 |
| 0.153         | 36.97 | 296  | 0.4584          | 0.7615   | 0.7691    | 0.7615 | 0.7633 |
| 0.1427        | 37.97 | 304  | 0.4608          | 0.7798   | 0.7830    | 0.7798 | 0.7796 |
| 0.113         | 38.97 | 312  | 0.4571          | 0.7890   | 0.7922    | 0.7890 | 0.7899 |
| 0.1146        | 39.97 | 320  | 0.5270          | 0.7615   | 0.7651    | 0.7615 | 0.7613 |
| 0.1146        | 40.97 | 328  | 0.4888          | 0.7706   | 0.7782    | 0.7706 | 0.7710 |
| 0.1275        | 41.97 | 336  | 0.4523          | 0.7890   | 0.7809    | 0.7890 | 0.7837 |
| 0.0959        | 42.97 | 344  | 0.4697          | 0.7798   | 0.7753    | 0.7798 | 0.7767 |
| 0.0882        | 43.97 | 352  | 0.4286          | 0.7706   | 0.7686    | 0.7706 | 0.7686 |
| 0.0847        | 44.97 | 360  | 0.5317          | 0.7890   | 0.7993    | 0.7890 | 0.7925 |
| 0.0847        | 45.97 | 368  | 0.5431          | 0.7615   | 0.7700    | 0.7615 | 0.7647 |
| 0.0813        | 46.97 | 376  | 0.4432          | 0.8257   | 0.8435    | 0.8257 | 0.8284 |
| 0.0768        | 47.97 | 384  | 0.4886          | 0.7982   | 0.8005    | 0.7982 | 0.7956 |
| 0.0627        | 48.97 | 392  | 0.5373          | 0.7982   | 0.8072    | 0.7982 | 0.8010 |
| 0.0688        | 49.97 | 400  | 0.5897          | 0.7798   | 0.7892    | 0.7798 | 0.7822 |
| 0.0688        | 50.97 | 408  | 0.5115          | 0.7982   | 0.8015    | 0.7982 | 0.7992 |
| 0.0676        | 51.97 | 416  | 0.4881          | 0.7982   | 0.7998    | 0.7982 | 0.7978 |
| 0.0539        | 52.97 | 424  | 0.4820          | 0.8073   | 0.8139    | 0.8073 | 0.8077 |
| 0.0596        | 53.97 | 432  | 0.4450          | 0.8257   | 0.8246    | 0.8257 | 0.8244 |
| 0.0611        | 54.97 | 440  | 0.5057          | 0.7890   | 0.8008    | 0.7890 | 0.7924 |
| 0.0611        | 55.97 | 448  | 0.4918          | 0.7982   | 0.8056    | 0.7982 | 0.8008 |
| 0.0643        | 56.97 | 456  | 0.5946          | 0.7523   | 0.7587    | 0.7523 | 0.7545 |
| 0.0605        | 57.97 | 464  | 0.4888          | 0.8073   | 0.8239    | 0.8073 | 0.8121 |
| 0.063         | 58.97 | 472  | 0.5917          | 0.7890   | 0.8051    | 0.7890 | 0.7937 |
| 0.0595        | 59.97 | 480  | 0.5117          | 0.7890   | 0.7904    | 0.7890 | 0.7894 |
| 0.0595        | 60.97 | 488  | 0.5497          | 0.7615   | 0.7692    | 0.7615 | 0.7635 |
| 0.0554        | 61.97 | 496  | 0.4742          | 0.8165   | 0.8101    | 0.8165 | 0.8126 |
| 0.0557        | 62.97 | 504  | 0.5369          | 0.7890   | 0.7886    | 0.7890 | 0.7886 |
| 0.0539        | 63.97 | 512  | 0.5440          | 0.7890   | 0.7922    | 0.7890 | 0.7899 |
| 0.048         | 64.97 | 520  | 0.5924          | 0.7890   | 0.7878    | 0.7890 | 0.7883 |
| 0.048         | 65.97 | 528  | 0.4863          | 0.8440   | 0.8440    | 0.8440 | 0.8440 |
| 0.045         | 66.97 | 536  | 0.5850          | 0.8073   | 0.8076    | 0.8073 | 0.8047 |
| 0.047         | 67.97 | 544  | 0.4939          | 0.8257   | 0.8212    | 0.8257 | 0.8227 |
| 0.0412        | 68.97 | 552  | 0.4850          | 0.7890   | 0.7912    | 0.7890 | 0.7900 |
| 0.0392        | 69.97 | 560  | 0.5066          | 0.8257   | 0.8265    | 0.8257 | 0.8258 |
| 0.0392        | 70.97 | 568  | 0.4965          | 0.8073   | 0.8053    | 0.8073 | 0.8058 |
| 0.0423        | 71.97 | 576  | 0.4717          | 0.8349   | 0.8376    | 0.8349 | 0.8351 |
| 0.0471        | 72.97 | 584  | 0.4845          | 0.8257   | 0.8378    | 0.8257 | 0.8296 |
| 0.0322        | 73.97 | 592  | 0.5188          | 0.7706   | 0.7689    | 0.7706 | 0.7693 |
| 0.042         | 74.97 | 600  | 0.5242          | 0.7706   | 0.7699    | 0.7706 | 0.7701 |
| 0.042         | 75.97 | 608  | 0.5945          | 0.7798   | 0.7824    | 0.7798 | 0.7804 |
| 0.0416        | 76.97 | 616  | 0.5432          | 0.7982   | 0.8038    | 0.7982 | 0.7993 |
| 0.0399        | 77.97 | 624  | 0.5381          | 0.7982   | 0.8072    | 0.7982 | 0.7994 |
| 0.0439        | 78.97 | 632  | 0.6181          | 0.7798   | 0.7878    | 0.7798 | 0.7827 |
| 0.0462        | 79.97 | 640  | 0.4801          | 0.8165   | 0.8182    | 0.8165 | 0.8173 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "microsoft-resnet-50-cartoon-emotion-detection", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8165137614678899, "name": "Accuracy"}, {"type": "precision", "value": 0.8181998512273742, "name": "Precision"}, {"type": "recall", "value": 0.8165137614678899, "name": "Recall"}, {"type": "f1", "value": 0.8172526992448356, "name": "F1"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

microsoft-resnet-50-cartoon-emotion-detection
=============================================


This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4801
* Accuracy: 0.8165
* Precision: 0.8182
* Recall: 0.8165
* F1: 0.8173


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00012
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.11.0