
# vit-base-patch16-224-Futurama_Image_multilabel_clf

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224).

It achieves the following results on the evaluation set:
- Loss: 0.0592
- F1: 0.9818
- Roc Auc: 0.9842
- Accuracy: 0.9672

## Model description

This is a multilabel classification model of screenshot images from the show Futurama.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multilabel%20Classification/Futurama%20Screenshots/Futurama%20-%20ML%20Image%20CLF.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/gonzalorecioc/futurama-frames-with-characteronscreen-data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2456        | 1.0   | 916  | 0.0723          | 0.9711 | 0.9746  | 0.9481   |
| 0.0269        | 2.0   | 1832 | 0.0545          | 0.9799 | 0.9818  | 0.9640   |
| 0.0086        | 3.0   | 2748 | 0.0580          | 0.9794 | 0.9814  | 0.9623   |
| 0.0044        | 4.0   | 3664 | 0.0612          | 0.9814 | 0.9832  | 0.9651   |
| 0.0027        | 5.0   | 4580 | 0.0592          | 0.9818 | 0.9842  | 0.9672   |
| 0.0017        | 6.0   | 5496 | 0.0634          | 0.9800 | 0.9832  | 0.9645   |
| 0.0012        | 7.0   | 6412 | 0.0657          | 0.9817 | 0.9840  | 0.9667   |
| 0.0005        | 8.0   | 7328 | 0.0668          | 0.9812 | 0.9836  | 0.9667   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["f1", "accuracy", "roc_auc"], "pipeline_tag": "image-classification", "model-index": [{"name": "vit-base-patch16-224-Futurama_Image_multilabel_clf", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "img", "split": "train", "args": "img"}, "metrics": [{"type": "f1", "value": 0.98175456135966, "name": "F1"}, {"type": "accuracy", "value": 0.9672489082969432, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer en dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-base-patch16-224-Futurama\_Image\_multilabel\_clf
=====================================================


This model is a fine-tuned version of google/vit-base-patch16-224.


It achieves the following results on the evaluation set:


* Loss: 0.0592
* F1: 0.9818
* Roc Auc: 0.9842
* Accuracy: 0.9672


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


This is a multilabel classification model of screenshot images from the show Futurama.


For more information on how it was created, check out the following link: URL


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


This model is intended to demonstrate my ability to solve a complex problem using technology.


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


Dataset Source: URL


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.12.1