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# microsoft-resnet-50-cartoon-face-recognition

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: 0.8508
- Accuracy: 0.7755
- Precision: 0.7715
- Recall: 0.7755
- F1: 0.7676

## 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: 120

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.89   | 6    | 3.1774          | 0.0370   | 0.0069    | 0.0370 | 0.0098 |
| 3.4185        | 1.89   | 12   | 3.1739          | 0.0301   | 0.0100    | 0.0301 | 0.0126 |
| 3.4185        | 2.89   | 18   | 3.1668          | 0.0440   | 0.0805    | 0.0440 | 0.0340 |
| 3.6463        | 3.89   | 24   | 3.1583          | 0.0370   | 0.0180    | 0.0370 | 0.0151 |
| 3.3899        | 4.89   | 30   | 3.1425          | 0.0741   | 0.0610    | 0.0741 | 0.0453 |
| 3.3899        | 5.89   | 36   | 3.1262          | 0.0856   | 0.0334    | 0.0856 | 0.0405 |
| 3.5947        | 6.89   | 42   | 3.1055          | 0.1019   | 0.0784    | 0.1019 | 0.0481 |
| 3.5947        | 7.89   | 48   | 3.0841          | 0.1181   | 0.1071    | 0.1181 | 0.0500 |
| 3.5553        | 8.89   | 54   | 3.0650          | 0.1065   | 0.0216    | 0.1065 | 0.0343 |
| 3.2713        | 9.89   | 60   | 3.0351          | 0.1273   | 0.0323    | 0.1273 | 0.0418 |
| 3.2713        | 10.89  | 66   | 3.0069          | 0.1227   | 0.0311    | 0.1227 | 0.0390 |
| 3.4382        | 11.89  | 72   | 2.9754          | 0.1204   | 0.0353    | 0.1204 | 0.0366 |
| 3.4382        | 12.89  | 78   | 2.9455          | 0.1227   | 0.0224    | 0.1227 | 0.0338 |
| 3.3573        | 13.89  | 84   | 2.9167          | 0.1204   | 0.0213    | 0.1204 | 0.0332 |
| 3.0549        | 14.89  | 90   | 2.8841          | 0.1227   | 0.0474    | 0.1227 | 0.0408 |
| 3.0549        | 15.89  | 96   | 2.8534          | 0.1412   | 0.1174    | 0.1412 | 0.0540 |
| 3.1853        | 16.89  | 102  | 2.8143          | 0.1505   | 0.1595    | 0.1505 | 0.0667 |
| 3.1853        | 17.89  | 108  | 2.7771          | 0.1667   | 0.1693    | 0.1667 | 0.0719 |
| 3.0871        | 18.89  | 114  | 2.7400          | 0.1759   | 0.1454    | 0.1759 | 0.0896 |
| 2.7666        | 19.89  | 120  | 2.7048          | 0.2014   | 0.0927    | 0.2014 | 0.1051 |
| 2.7666        | 20.89  | 126  | 2.6458          | 0.2315   | 0.1622    | 0.2315 | 0.1250 |
| 2.846         | 21.89  | 132  | 2.5803          | 0.2569   | 0.2386    | 0.2569 | 0.1470 |
| 2.846         | 22.89  | 138  | 2.5291          | 0.2639   | 0.2725    | 0.2639 | 0.1523 |
| 2.7428        | 23.89  | 144  | 2.4916          | 0.2870   | 0.2114    | 0.2870 | 0.1811 |
| 2.4183        | 24.89  | 150  | 2.4273          | 0.3079   | 0.2322    | 0.3079 | 0.2048 |
| 2.4183        | 25.89  | 156  | 2.3923          | 0.3194   | 0.2937    | 0.3194 | 0.2238 |
| 2.5064        | 26.89  | 162  | 2.3349          | 0.3403   | 0.3183    | 0.3403 | 0.2494 |
| 2.5064        | 27.89  | 168  | 2.2977          | 0.3542   | 0.3554    | 0.3542 | 0.2663 |
| 2.4046        | 28.89  | 174  | 2.2363          | 0.3773   | 0.3214    | 0.3773 | 0.2981 |
| 2.1201        | 29.89  | 180  | 2.1791          | 0.3889   | 0.4024    | 0.3889 | 0.3179 |
| 2.1201        | 30.89  | 186  | 2.1448          | 0.4144   | 0.4079    | 0.4144 | 0.3455 |
| 2.1705        | 31.89  | 192  | 2.0969          | 0.4306   | 0.4214    | 0.4306 | 0.3583 |
| 2.1705        | 32.89  | 198  | 2.0535          | 0.4468   | 0.4448    | 0.4468 | 0.3797 |
| 2.0295        | 33.89  | 204  | 1.9940          | 0.4745   | 0.4877    | 0.4745 | 0.4133 |
| 1.8114        | 34.89  | 210  | 1.9467          | 0.4861   | 0.4952    | 0.4861 | 0.4261 |
| 1.8114        | 35.89  | 216  | 1.8896          | 0.4931   | 0.4510    | 0.4931 | 0.4321 |
| 1.8048        | 36.89  | 222  | 1.8404          | 0.5046   | 0.4859    | 0.5046 | 0.4507 |
| 1.8048        | 37.89  | 228  | 1.7999          | 0.5278   | 0.5142    | 0.5278 | 0.4816 |
| 1.6862        | 38.89  | 234  | 1.7363          | 0.5324   | 0.5169    | 0.5324 | 0.4844 |
| 1.4545        | 39.89  | 240  | 1.7104          | 0.5440   | 0.5100    | 0.5440 | 0.4971 |
| 1.4545        | 40.89  | 246  | 1.6492          | 0.5648   | 0.5239    | 0.5648 | 0.5138 |
| 1.4444        | 41.89  | 252  | 1.6076          | 0.5671   | 0.5329    | 0.5671 | 0.5260 |
| 1.4444        | 42.89  | 258  | 1.5784          | 0.5741   | 0.5708    | 0.5741 | 0.5424 |
| 1.3124        | 43.89  | 264  | 1.5259          | 0.6019   | 0.5977    | 0.6019 | 0.5619 |
| 1.1645        | 44.89  | 270  | 1.4814          | 0.6181   | 0.6033    | 0.6181 | 0.5880 |
| 1.1645        | 45.89  | 276  | 1.4697          | 0.6088   | 0.6033    | 0.6088 | 0.5803 |
| 1.1307        | 46.89  | 282  | 1.4380          | 0.6088   | 0.6015    | 0.6088 | 0.5769 |
| 1.1307        | 47.89  | 288  | 1.3872          | 0.6227   | 0.6085    | 0.6227 | 0.5917 |
| 1.0347        | 48.89  | 294  | 1.3709          | 0.6157   | 0.6039    | 0.6157 | 0.5880 |
| 0.8962        | 49.89  | 300  | 1.3415          | 0.6296   | 0.6120    | 0.6296 | 0.6057 |
| 0.8962        | 50.89  | 306  | 1.3290          | 0.6389   | 0.6327    | 0.6389 | 0.6134 |
| 0.8898        | 51.89  | 312  | 1.2836          | 0.6389   | 0.6192    | 0.6389 | 0.6119 |
| 0.8898        | 52.89  | 318  | 1.2665          | 0.6412   | 0.6186    | 0.6412 | 0.6162 |
| 0.7886        | 53.89  | 324  | 1.2272          | 0.6551   | 0.6431    | 0.6551 | 0.6319 |
| 0.6794        | 54.89  | 330  | 1.2144          | 0.6806   | 0.6643    | 0.6806 | 0.6629 |
| 0.6794        | 55.89  | 336  | 1.1817          | 0.6806   | 0.6666    | 0.6806 | 0.6642 |
| 0.6459        | 56.89  | 342  | 1.1702          | 0.6782   | 0.6591    | 0.6782 | 0.6574 |
| 0.6459        | 57.89  | 348  | 1.0947          | 0.7037   | 0.6863    | 0.7037 | 0.6883 |
| 0.6075        | 58.89  | 354  | 1.1227          | 0.7037   | 0.6874    | 0.7037 | 0.6867 |
| 0.4979        | 59.89  | 360  | 1.0849          | 0.7083   | 0.6813    | 0.7083 | 0.6895 |
| 0.4979        | 60.89  | 366  | 1.0742          | 0.7153   | 0.6924    | 0.7153 | 0.6976 |
| 0.4895        | 61.89  | 372  | 1.0452          | 0.7245   | 0.7020    | 0.7245 | 0.7057 |
| 0.4895        | 62.89  | 378  | 1.0435          | 0.7361   | 0.7316    | 0.7361 | 0.7235 |
| 0.456         | 63.89  | 384  | 1.0698          | 0.6921   | 0.6835    | 0.6921 | 0.6783 |
| 0.3816        | 64.89  | 390  | 1.0126          | 0.7222   | 0.7064    | 0.7222 | 0.7091 |
| 0.3816        | 65.89  | 396  | 0.9934          | 0.7361   | 0.7247    | 0.7361 | 0.7205 |
| 0.3599        | 66.89  | 402  | 0.9960          | 0.7292   | 0.7213    | 0.7292 | 0.7170 |
| 0.3599        | 67.89  | 408  | 1.0141          | 0.7222   | 0.7148    | 0.7222 | 0.7087 |
| 0.3484        | 68.89  | 414  | 0.9934          | 0.7222   | 0.7125    | 0.7222 | 0.7107 |
| 0.2939        | 69.89  | 420  | 0.9835          | 0.7431   | 0.7417    | 0.7431 | 0.7349 |
| 0.2939        | 70.89  | 426  | 0.9870          | 0.7315   | 0.7275    | 0.7315 | 0.7217 |
| 0.285         | 71.89  | 432  | 0.9656          | 0.7431   | 0.7411    | 0.7431 | 0.7340 |
| 0.285         | 72.89  | 438  | 0.9462          | 0.7338   | 0.7320    | 0.7338 | 0.7267 |
| 0.2463        | 73.89  | 444  | 0.9513          | 0.7454   | 0.7467    | 0.7454 | 0.7384 |
| 0.2328        | 74.89  | 450  | 0.9334          | 0.7361   | 0.7389    | 0.7361 | 0.7286 |
| 0.2328        | 75.89  | 456  | 0.9375          | 0.7384   | 0.7278    | 0.7384 | 0.7291 |
| 0.2208        | 76.89  | 462  | 0.9332          | 0.7407   | 0.7357    | 0.7407 | 0.7322 |
| 0.2208        | 77.89  | 468  | 0.9408          | 0.7384   | 0.7406    | 0.7384 | 0.7346 |
| 0.2177        | 78.89  | 474  | 0.9059          | 0.7222   | 0.7183    | 0.7222 | 0.7136 |
| 0.1734        | 79.89  | 480  | 0.9517          | 0.7315   | 0.7371    | 0.7315 | 0.7257 |
| 0.1734        | 80.89  | 486  | 0.9063          | 0.7523   | 0.7462    | 0.7523 | 0.7424 |
| 0.1791        | 81.89  | 492  | 0.9171          | 0.7454   | 0.7461    | 0.7454 | 0.7386 |
| 0.1791        | 82.89  | 498  | 0.8846          | 0.7523   | 0.7561    | 0.7523 | 0.7485 |
| 0.1681        | 83.89  | 504  | 0.8871          | 0.7384   | 0.7431    | 0.7384 | 0.7320 |
| 0.1573        | 84.89  | 510  | 0.9118          | 0.7454   | 0.7474    | 0.7454 | 0.7395 |
| 0.1573        | 85.89  | 516  | 0.9006          | 0.7407   | 0.7432    | 0.7407 | 0.7366 |
| 0.1439        | 86.89  | 522  | 0.8703          | 0.7616   | 0.7693    | 0.7616 | 0.7579 |
| 0.1439        | 87.89  | 528  | 0.8988          | 0.7454   | 0.7570    | 0.7454 | 0.7401 |
| 0.1362        | 88.89  | 534  | 0.9234          | 0.7454   | 0.7477    | 0.7454 | 0.7396 |
| 0.1249        | 89.89  | 540  | 0.8860          | 0.75     | 0.7473    | 0.75   | 0.7425 |
| 0.1249        | 90.89  | 546  | 0.8608          | 0.7546   | 0.7601    | 0.7546 | 0.7513 |
| 0.1264        | 91.89  | 552  | 0.8871          | 0.7593   | 0.7640    | 0.7593 | 0.7560 |
| 0.1264        | 92.89  | 558  | 0.8432          | 0.7639   | 0.7727    | 0.7639 | 0.7599 |
| 0.1201        | 93.89  | 564  | 0.8654          | 0.7639   | 0.7698    | 0.7639 | 0.7569 |
| 0.1117        | 94.89  | 570  | 0.8856          | 0.7454   | 0.7569    | 0.7454 | 0.7415 |
| 0.1117        | 95.89  | 576  | 0.8668          | 0.7546   | 0.7686    | 0.7546 | 0.7535 |
| 0.1128        | 96.89  | 582  | 0.8630          | 0.7662   | 0.7698    | 0.7662 | 0.7619 |
| 0.1128        | 97.89  | 588  | 0.8551          | 0.7731   | 0.7826    | 0.7731 | 0.7696 |
| 0.1155        | 98.89  | 594  | 0.8697          | 0.7708   | 0.7738    | 0.7708 | 0.7643 |
| 0.0987        | 99.89  | 600  | 0.8613          | 0.7546   | 0.7518    | 0.7546 | 0.7484 |
| 0.0987        | 100.89 | 606  | 0.8742          | 0.7569   | 0.7597    | 0.7569 | 0.7524 |
| 0.1063        | 101.89 | 612  | 0.8498          | 0.7755   | 0.7807    | 0.7755 | 0.7712 |
| 0.1063        | 102.89 | 618  | 0.8557          | 0.7708   | 0.7749    | 0.7708 | 0.7655 |
| 0.097         | 103.89 | 624  | 0.8764          | 0.7546   | 0.7634    | 0.7546 | 0.7527 |
| 0.0947        | 104.89 | 630  | 0.8677          | 0.7616   | 0.7628    | 0.7616 | 0.7572 |
| 0.0947        | 105.89 | 636  | 0.8909          | 0.75     | 0.7614    | 0.75   | 0.7469 |
| 0.1013        | 106.89 | 642  | 0.8283          | 0.7639   | 0.7621    | 0.7639 | 0.7580 |
| 0.1013        | 107.89 | 648  | 0.8471          | 0.7662   | 0.7864    | 0.7662 | 0.7651 |
| 0.0963        | 108.89 | 654  | 0.8653          | 0.7593   | 0.7701    | 0.7593 | 0.7558 |
| 0.0874        | 109.89 | 660  | 0.8479          | 0.7731   | 0.7834    | 0.7731 | 0.7692 |
| 0.0874        | 110.89 | 666  | 0.8584          | 0.7639   | 0.7719    | 0.7639 | 0.7620 |
| 0.0876        | 111.89 | 672  | 0.8714          | 0.7616   | 0.7600    | 0.7616 | 0.7550 |
| 0.0876        | 112.89 | 678  | 0.8509          | 0.7731   | 0.7847    | 0.7731 | 0.7727 |
| 0.0974        | 113.89 | 684  | 0.8688          | 0.7685   | 0.7741    | 0.7685 | 0.7648 |
| 0.0869        | 114.89 | 690  | 0.8590          | 0.7847   | 0.7932    | 0.7847 | 0.7794 |
| 0.0869        | 115.89 | 696  | 0.8687          | 0.7593   | 0.7703    | 0.7593 | 0.7579 |
| 0.0877        | 116.89 | 702  | 0.8735          | 0.7593   | 0.7698    | 0.7593 | 0.7554 |
| 0.0877        | 117.89 | 708  | 0.8566          | 0.7546   | 0.7732    | 0.7546 | 0.7518 |
| 0.0883        | 118.89 | 714  | 0.8681          | 0.7569   | 0.7591    | 0.7569 | 0.7525 |
| 0.0762        | 119.89 | 720  | 0.8508          | 0.7755   | 0.7715    | 0.7755 | 0.7676 |


### 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-face-recognition", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7754629629629629, "name": "Accuracy"}, {"type": "precision", "value": 0.7715337787579363, "name": "Precision"}, {"type": "recall", "value": 0.7754629629629629, "name": "Recall"}, {"type": "f1", "value": 0.7676128880040883, "name": "F1"}]}]}]}
transformers pytorch tensorboard resnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #resnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

microsoft-resnet-50-cartoon-face-recognition
============================================


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: 0.8508
* Accuracy: 0.7755
* Precision: 0.7715
* Recall: 0.7755
* F1: 0.7676


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: 120


### 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: 120 ### 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 #has_space #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: 120 ### 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 #has_space #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: 120### Training results### Framework versions


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