
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ScCvT_fold_5

This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3026
- F1: 0.9100
- Roc Auc: 0.9850
- Accuracy: 0.9103

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 65

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 1.8858        | 1.0   | 60   | 1.8336          | 0.3756 | 0.6770  | 0.3062   |
| 1.7573        | 2.0   | 120  | 1.5748          | 0.5904 | 0.8013  | 0.5342   |
| 1.6042        | 3.0   | 180  | 1.2448          | 0.6648 | 0.8782  | 0.6270   |
| 1.4011        | 4.0   | 240  | 1.0728          | 0.6856 | 0.9162  | 0.6458   |
| 1.3588        | 5.0   | 300  | 0.8680          | 0.7432 | 0.9329  | 0.7131   |
| 1.2291        | 6.0   | 360  | 0.8431          | 0.7378 | 0.9400  | 0.7053   |
| 1.145         | 7.0   | 420  | 0.8501          | 0.7355 | 0.9453  | 0.6974   |
| 1.0652        | 8.0   | 480  | 0.7471          | 0.7559 | 0.9533  | 0.7251   |
| 1.0174        | 9.0   | 540  | 0.5592          | 0.8140 | 0.9634  | 0.8002   |
| 0.892         | 10.0  | 600  | 0.6785          | 0.7726 | 0.9614  | 0.7480   |
| 0.8584        | 11.0  | 660  | 0.5690          | 0.8088 | 0.9676  | 0.7898   |
| 0.8662        | 12.0  | 720  | 0.6049          | 0.7911 | 0.9696  | 0.7679   |
| 0.9131        | 13.0  | 780  | 0.4984          | 0.8295 | 0.9717  | 0.8179   |
| 0.8616        | 14.0  | 840  | 0.4755          | 0.8301 | 0.9722  | 0.8190   |
| 0.8398        | 15.0  | 900  | 0.5121          | 0.8237 | 0.9721  | 0.8101   |
| 0.6746        | 16.0  | 960  | 0.4823          | 0.8322 | 0.9742  | 0.8185   |
| 0.779         | 17.0  | 1020 | 0.5121          | 0.8193 | 0.9749  | 0.8002   |
| 0.7436        | 18.0  | 1080 | 0.4911          | 0.8270 | 0.9740  | 0.8153   |
| 0.7586        | 19.0  | 1140 | 0.4376          | 0.8439 | 0.9769  | 0.8346   |
| 0.688         | 20.0  | 1200 | 0.4732          | 0.8377 | 0.9784  | 0.8247   |
| 0.8294        | 21.0  | 1260 | 0.4889          | 0.8323 | 0.9777  | 0.8138   |
| 0.7451        | 22.0  | 1320 | 0.4015          | 0.8605 | 0.9787  | 0.8545   |
| 0.708         | 23.0  | 1380 | 0.3818          | 0.8677 | 0.9800  | 0.8612   |
| 0.6112        | 24.0  | 1440 | 0.4229          | 0.8651 | 0.9781  | 0.8565   |
| 0.6936        | 25.0  | 1500 | 0.3678          | 0.8714 | 0.9809  | 0.8670   |
| 0.6486        | 26.0  | 1560 | 0.3565          | 0.8722 | 0.9807  | 0.8685   |
| 0.6078        | 27.0  | 1620 | 0.3489          | 0.8760 | 0.9815  | 0.8722   |
| 0.6513        | 28.0  | 1680 | 0.3546          | 0.8774 | 0.9821  | 0.8727   |
| 0.6562        | 29.0  | 1740 | 0.3420          | 0.8816 | 0.9817  | 0.8774   |
| 0.6561        | 30.0  | 1800 | 0.3649          | 0.8745 | 0.9818  | 0.8691   |
| 0.6056        | 31.0  | 1860 | 0.3692          | 0.8730 | 0.9823  | 0.8670   |
| 0.5927        | 32.0  | 1920 | 0.3584          | 0.8825 | 0.9822  | 0.8779   |
| 0.6021        | 33.0  | 1980 | 0.3345          | 0.8870 | 0.9821  | 0.8842   |
| 0.6052        | 34.0  | 2040 | 0.3388          | 0.8887 | 0.9820  | 0.8868   |
| 0.6026        | 35.0  | 2100 | 0.3161          | 0.8938 | 0.9831  | 0.8925   |
| 0.5735        | 36.0  | 2160 | 0.3324          | 0.8952 | 0.9825  | 0.8957   |
| 0.6058        | 37.0  | 2220 | 0.3262          | 0.8932 | 0.9829  | 0.8905   |
| 0.5648        | 38.0  | 2280 | 0.3243          | 0.8931 | 0.9834  | 0.8899   |
| 0.6248        | 39.0  | 2340 | 0.3499          | 0.8825 | 0.9837  | 0.8769   |
| 0.4926        | 40.0  | 2400 | 0.3066          | 0.9045 | 0.9837  | 0.9045   |
| 0.5341        | 41.0  | 2460 | 0.3037          | 0.9042 | 0.9835  | 0.9061   |
| 0.5215        | 42.0  | 2520 | 0.3193          | 0.8968 | 0.9832  | 0.8967   |
| 0.5892        | 43.0  | 2580 | 0.3276          | 0.8910 | 0.9837  | 0.8884   |
| 0.559         | 44.0  | 2640 | 0.3129          | 0.9006 | 0.9843  | 0.8988   |
| 0.5306        | 45.0  | 2700 | 0.3137          | 0.9024 | 0.9839  | 0.9035   |
| 0.4789        | 46.0  | 2760 | 0.3128          | 0.8984 | 0.9842  | 0.8972   |
| 0.5518        | 47.0  | 2820 | 0.3117          | 0.9057 | 0.9838  | 0.9051   |
| 0.5201        | 48.0  | 2880 | 0.3110          | 0.9023 | 0.9842  | 0.9014   |
| 0.5698        | 49.0  | 2940 | 0.3031          | 0.9074 | 0.9841  | 0.9071   |
| 0.5227        | 50.0  | 3000 | 0.3343          | 0.9007 | 0.9834  | 0.8988   |
| 0.5416        | 51.0  | 3060 | 0.3117          | 0.9078 | 0.9837  | 0.9082   |
| 0.5882        | 52.0  | 3120 | 0.3132          | 0.9049 | 0.9835  | 0.9051   |
| 0.4286        | 53.0  | 3180 | 0.3133          | 0.9064 | 0.9837  | 0.9066   |
| 0.5278        | 54.0  | 3240 | 0.3050          | 0.9080 | 0.9847  | 0.9077   |
| 0.586         | 55.0  | 3300 | 0.3063          | 0.9065 | 0.9842  | 0.9082   |
| 0.4708        | 56.0  | 3360 | 0.3119          | 0.9058 | 0.9840  | 0.9056   |
| 0.5512        | 57.0  | 3420 | 0.3140          | 0.9023 | 0.9842  | 0.9014   |
| 0.5535        | 58.0  | 3480 | 0.3079          | 0.9046 | 0.9847  | 0.9045   |
| 0.4817        | 59.0  | 3540 | 0.3077          | 0.9061 | 0.9845  | 0.9061   |
| 0.5381        | 60.0  | 3600 | 0.3122          | 0.9015 | 0.9848  | 0.9009   |
| 0.6305        | 61.0  | 3660 | 0.3026          | 0.9100 | 0.9850  | 0.9103   |
| 0.4658        | 62.0  | 3720 | 0.3044          | 0.9093 | 0.9851  | 0.9092   |
| 0.4873        | 63.0  | 3780 | 0.3076          | 0.9074 | 0.9849  | 0.9071   |
| 0.5791        | 64.0  | 3840 | 0.3045          | 0.9077 | 0.9850  | 0.9077   |
| 0.5871        | 65.0  | 3900 | 0.3028          | 0.9076 | 0.9852  | 0.9071   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "ScCvT_fold_5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "f1", "value": 0.909963393596575, "name": "F1"}, {"type": "accuracy", "value": 0.9102764736567553, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard cvt image-classification generated_from_trainer dataset:imagefolder doi:10.57967/hf/0875 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #cvt #image-classification #generated_from_trainer #dataset-imagefolder #doi-10.57967/hf/0875 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

ScCvT\_fold\_5
==============


This model is a fine-tuned version of microsoft/cvt-13 on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3026
* F1: 0.9100
* Roc Auc: 0.9850
* Accuracy: 0.9103


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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 65


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 65 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #cvt #image-classification #generated_from_trainer #dataset-imagefolder #doi-10.57967/hf/0875 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 65 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3