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# vit-KAIYI

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.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- F1: 0.6998
- Roc Auc: 0.8236

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.4021        | 1.0   | 506   | 0.3330          | 0.5653 | 0.7446  |
| 0.244         | 2.0   | 1012  | 0.2762          | 0.6360 | 0.7862  |
| 0.1793        | 3.0   | 1518  | 0.2503          | 0.6857 | 0.8154  |
| 0.1382        | 4.0   | 2024  | 0.2399          | 0.6991 | 0.8232  |
| 0.1089        | 5.0   | 2530  | 0.2336          | 0.7010 | 0.8244  |
| 0.0869        | 6.0   | 3036  | 0.2307          | 0.7092 | 0.8291  |
| 0.0698        | 7.0   | 3542  | 0.2318          | 0.7134 | 0.8316  |
| 0.0563        | 8.0   | 4048  | 0.2402          | 0.7076 | 0.8282  |
| 0.0459        | 9.0   | 4554  | 0.2402          | 0.7040 | 0.8261  |
| 0.0371        | 10.0  | 5060  | 0.2514          | 0.7053 | 0.8269  |
| 0.0306        | 11.0  | 5566  | 0.2582          | 0.7065 | 0.8275  |
| 0.0249        | 12.0  | 6072  | 0.2672          | 0.7050 | 0.8267  |
| 0.0206        | 13.0  | 6578  | 0.2735          | 0.7060 | 0.8273  |
| 0.0171        | 14.0  | 7084  | 0.2822          | 0.7039 | 0.8261  |
| 0.0142        | 15.0  | 7590  | 0.2934          | 0.7085 | 0.8288  |
| 0.0122        | 16.0  | 8096  | 0.2980          | 0.7066 | 0.8276  |
| 0.0104        | 17.0  | 8602  | 0.3126          | 0.6994 | 0.8234  |
| 0.0087        | 18.0  | 9108  | 0.3213          | 0.7040 | 0.8261  |
| 0.0074        | 19.0  | 9614  | 0.3245          | 0.7042 | 0.8262  |
| 0.0063        | 20.0  | 10120 | 0.3412          | 0.7038 | 0.8260  |
| 0.0053        | 21.0  | 10626 | 0.3525          | 0.7031 | 0.8256  |
| 0.0044        | 22.0  | 11132 | 0.3584          | 0.7038 | 0.8260  |
| 0.0039        | 23.0  | 11638 | 0.3710          | 0.7026 | 0.8253  |
| 0.0033        | 24.0  | 12144 | 0.3787          | 0.7020 | 0.8249  |
| 0.0029        | 25.0  | 12650 | 0.3867          | 0.7023 | 0.8251  |
| 0.0025        | 26.0  | 13156 | 0.3946          | 0.7015 | 0.8246  |
| 0.0023        | 27.0  | 13662 | 0.4052          | 0.7004 | 0.8240  |
| 0.002         | 28.0  | 14168 | 0.4115          | 0.6996 | 0.8235  |
| 0.0018        | 29.0  | 14674 | 0.4196          | 0.7005 | 0.8240  |
| 0.0016        | 30.0  | 15180 | 0.4360          | 0.7013 | 0.8245  |
| 0.0014        | 31.0  | 15686 | 0.4297          | 0.7008 | 0.8242  |
| 0.0013        | 32.0  | 16192 | 0.4444          | 0.6980 | 0.8226  |
| 0.0012        | 33.0  | 16698 | 0.4420          | 0.7010 | 0.8243  |
| 0.0011        | 34.0  | 17204 | 0.4468          | 0.6998 | 0.8237  |
| 0.001         | 35.0  | 17710 | 0.4529          | 0.7005 | 0.8240  |
| 0.001         | 36.0  | 18216 | 0.4566          | 0.7000 | 0.8237  |
| 0.0009        | 37.0  | 18722 | 0.4599          | 0.6997 | 0.8236  |
| 0.0009        | 38.0  | 19228 | 0.4621          | 0.6999 | 0.8237  |
| 0.0009        | 39.0  | 19734 | 0.4649          | 0.6993 | 0.8233  |
| 0.0008        | 40.0  | 20240 | 0.4642          | 0.6998 | 0.8236  |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["f1"], "model-index": [{"name": "vit-KAIYI", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "f1", "value": 0.6997885338345865, "name": "F1"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-KAIYI
=========


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4642
* F1: 0.6998
* Roc Auc: 0.8236


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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3