
# dit-base-Business_Documents_Classified_v2

This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6715
- Accuracy: 0.826
- Weighted f1: 0.8272
- Micro f1: 0.826
- Macro f1: 0.8242
- Weighted recall: 0.826
- Micro recall: 0.826
- Macro recall: 0.8237
- Weighted precision: 0.8327
- Micro precision: 0.826
- Macro precision: 0.8293

## Model description

This is a classification model of 16 different types of documents.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Real%20World%20Documents%20Collections/Real%20World%20Documents%20Collections_v2.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/shaz13/real-world-documents-collections

## 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: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 2.7266        | 0.99  | 31   | 2.4738          | 0.208    | 0.1811      | 0.208    | 0.1827   | 0.208           | 0.208        | 0.2101       | 0.2143             | 0.208           | 0.2246          |
| 2.171         | 1.98  | 62   | 1.8510          | 0.423    | 0.3936      | 0.4230   | 0.3925   | 0.423           | 0.423        | 0.4243       | 0.4503             | 0.423           | 0.4446          |
| 1.6525        | 2.98  | 93   | 1.2633          | 0.61     | 0.5884      | 0.61     | 0.5855   | 0.61            | 0.61         | 0.6124       | 0.6377             | 0.61            | 0.6283          |
| 1.346         | 4.0   | 125  | 1.0259          | 0.706    | 0.7023      | 0.706    | 0.6992   | 0.706           | 0.706        | 0.7058       | 0.7095             | 0.706           | 0.7034          |
| 1.253         | 4.99  | 156  | 0.9180          | 0.729    | 0.7277      | 0.729    | 0.7239   | 0.729           | 0.729        | 0.7291       | 0.7340             | 0.729           | 0.7261          |
| 1.0975        | 5.98  | 187  | 0.8859          | 0.747    | 0.7480      | 0.747    | 0.7437   | 0.747           | 0.747        | 0.7472       | 0.7609             | 0.747           | 0.7526          |
| 1.1122        | 6.98  | 218  | 0.8270          | 0.76     | 0.7606      | 0.76     | 0.7578   | 0.76            | 0.76         | 0.7594       | 0.7772             | 0.76            | 0.7727          |
| 1.0365        | 8.0   | 250  | 0.7806          | 0.775    | 0.7759      | 0.775    | 0.7730   | 0.775           | 0.775        | 0.7735       | 0.7957             | 0.775           | 0.7920          |
| 1.004         | 8.99  | 281  | 0.7472          | 0.796    | 0.7977      | 0.796    | 0.7957   | 0.796           | 0.796        | 0.7956       | 0.8193             | 0.796           | 0.8151          |
| 0.9278        | 9.98  | 312  | 0.7296          | 0.795    | 0.7974      | 0.795    | 0.7957   | 0.795           | 0.795        | 0.7953       | 0.8157             | 0.795           | 0.8115          |
| 0.8767        | 10.98 | 343  | 0.7257          | 0.809    | 0.8101      | 0.809    | 0.8078   | 0.809           | 0.809        | 0.8091       | 0.8182             | 0.809           | 0.8136          |
| 0.8656        | 12.0  | 375  | 0.6875          | 0.814    | 0.8137      | 0.8140   | 0.8106   | 0.814           | 0.814        | 0.8122       | 0.8207             | 0.814           | 0.8164          |
| 0.7905        | 12.99 | 406  | 0.7060          | 0.808    | 0.8093      | 0.808    | 0.8071   | 0.808           | 0.808        | 0.8068       | 0.8182             | 0.808           | 0.8145          |
| 0.8804        | 13.98 | 437  | 0.6849          | 0.82     | 0.8214      | 0.82     | 0.8183   | 0.82            | 0.82         | 0.8183       | 0.8260             | 0.82            | 0.8215          |
| 0.8265        | 14.98 | 468  | 0.6821          | 0.816    | 0.8171      | 0.816    | 0.8143   | 0.816           | 0.816        | 0.8142       | 0.8242             | 0.816           | 0.8206          |
| 0.7929        | 16.0  | 500  | 0.6877          | 0.818    | 0.8184      | 0.818    | 0.8152   | 0.818           | 0.818        | 0.8167       | 0.8240             | 0.818           | 0.8186          |
| 0.7993        | 16.99 | 531  | 0.6718          | 0.825    | 0.8259      | 0.825    | 0.8234   | 0.825           | 0.825        | 0.8227       | 0.8306             | 0.825           | 0.8282          |
| 0.7954        | 17.86 | 558  | 0.6715          | 0.826    | 0.8272      | 0.826    | 0.8242   | 0.826           | 0.826        | 0.8237       | 0.8327             | 0.826           | 0.8293          |

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "image-classification", "model-index": [{"name": "dit-base-Business_Documents_Classified_v2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "data", "split": "train", "args": "data"}, "metrics": [{"type": "accuracy", "value": 0.826, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard beit image-classification generated_from_trainer en dataset:imagefolder model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #en #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

dit-base-Business\_Documents\_Classified\_v2
============================================


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


* Loss: 0.6715
* Accuracy: 0.826
* Weighted f1: 0.8272
* Micro f1: 0.826
* Macro f1: 0.8242
* Weighted recall: 0.826
* Micro recall: 0.826
* Macro recall: 0.8237
* Weighted precision: 0.8327
* Micro precision: 0.826
* Macro precision: 0.8293


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


This is a classification model of 16 different types of documents.


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: 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: 18


### 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: 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: 18 ### 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 #beit #image-classification #generated_from_trainer #en #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 18 ### 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 #beit #image-classification #generated_from_trainer #en #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 18### Training results### Framework versions


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