---
base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: document-crop
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9021739130434783
---

<!-- 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. -->

# document-crop

This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7682
- Accuracy: 0.9022

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 250

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| 0.6337        | 2.6667   | 20   | 0.6879          | 0.5870   |
| 0.4336        | 5.3333   | 40   | 0.7280          | 0.6413   |
| 0.2493        | 8.0      | 60   | 0.5044          | 0.75     |
| 0.1756        | 10.6667  | 80   | 0.3750          | 0.8478   |
| 0.1715        | 13.3333  | 100  | 0.7468          | 0.6957   |
| 0.1525        | 16.0     | 120  | 0.6240          | 0.7935   |
| 0.2019        | 18.6667  | 140  | 0.3115          | 0.8804   |
| 0.1366        | 21.3333  | 160  | 0.8020          | 0.7391   |
| 0.1729        | 24.0     | 180  | 0.7651          | 0.7283   |
| 0.1499        | 26.6667  | 200  | 0.6695          | 0.7826   |
| 0.1226        | 29.3333  | 220  | 0.5607          | 0.8370   |
| 0.1426        | 32.0     | 240  | 0.5363          | 0.8152   |
| 0.0986        | 34.6667  | 260  | 0.2214          | 0.9022   |
| 0.0984        | 37.3333  | 280  | 0.2494          | 0.9022   |
| 0.1764        | 40.0     | 300  | 0.3202          | 0.9022   |
| 0.0712        | 42.6667  | 320  | 0.6895          | 0.8370   |
| 0.104         | 45.3333  | 340  | 0.8008          | 0.75     |
| 0.107         | 48.0     | 360  | 0.6523          | 0.8696   |
| 0.1446        | 50.6667  | 380  | 0.4615          | 0.8370   |
| 0.0525        | 53.3333  | 400  | 0.5936          | 0.9130   |
| 0.1076        | 56.0     | 420  | 0.5063          | 0.9022   |
| 0.0554        | 58.6667  | 440  | 0.4740          | 0.8913   |
| 0.0701        | 61.3333  | 460  | 0.4842          | 0.8587   |
| 0.1011        | 64.0     | 480  | 0.5180          | 0.8587   |
| 0.0471        | 66.6667  | 500  | 1.6979          | 0.7717   |
| 0.0559        | 69.3333  | 520  | 0.4181          | 0.9022   |
| 0.0371        | 72.0     | 540  | 0.4239          | 0.9022   |
| 0.0653        | 74.6667  | 560  | 0.2725          | 0.9239   |
| 0.0564        | 77.3333  | 580  | 0.8607          | 0.8043   |
| 0.0427        | 80.0     | 600  | 0.2848          | 0.9457   |
| 0.1251        | 82.6667  | 620  | 0.3903          | 0.9022   |
| 0.023         | 85.3333  | 640  | 0.4514          | 0.9239   |
| 0.0297        | 88.0     | 660  | 0.7634          | 0.8913   |
| 0.0553        | 90.6667  | 680  | 0.5395          | 0.8913   |
| 0.0147        | 93.3333  | 700  | 0.7752          | 0.8696   |
| 0.0804        | 96.0     | 720  | 0.6780          | 0.8913   |
| 0.0154        | 98.6667  | 740  | 0.7887          | 0.8587   |
| 0.0063        | 101.3333 | 760  | 0.5492          | 0.9239   |
| 0.0131        | 104.0    | 780  | 0.8119          | 0.8804   |
| 0.0113        | 106.6667 | 800  | 1.0839          | 0.8587   |
| 0.0268        | 109.3333 | 820  | 1.0396          | 0.8587   |
| 0.0215        | 112.0    | 840  | 0.8707          | 0.9022   |
| 0.0271        | 114.6667 | 860  | 0.5733          | 0.9457   |
| 0.0208        | 117.3333 | 880  | 0.6780          | 0.9130   |
| 0.0224        | 120.0    | 900  | 0.3565          | 0.9457   |
| 0.0324        | 122.6667 | 920  | 0.3860          | 0.9239   |
| 0.019         | 125.3333 | 940  | 0.5652          | 0.9022   |
| 0.0079        | 128.0    | 960  | 0.5316          | 0.9348   |
| 0.0064        | 130.6667 | 980  | 0.5368          | 0.9239   |
| 0.0055        | 133.3333 | 1000 | 0.8009          | 0.8913   |
| 0.0156        | 136.0    | 1020 | 0.8391          | 0.9348   |
| 0.04          | 138.6667 | 1040 | 0.6336          | 0.9022   |
| 0.0031        | 141.3333 | 1060 | 0.5656          | 0.9348   |
| 0.0009        | 144.0    | 1080 | 0.4957          | 0.9348   |
| 0.0004        | 146.6667 | 1100 | 0.9136          | 0.8913   |
| 0.006         | 149.3333 | 1120 | 0.9782          | 0.8913   |
| 0.0004        | 152.0    | 1140 | 0.9065          | 0.9239   |
| 0.0042        | 154.6667 | 1160 | 0.9944          | 0.9130   |
| 0.0001        | 157.3333 | 1180 | 0.8723          | 0.9239   |
| 0.0002        | 160.0    | 1200 | 1.1987          | 0.8804   |
| 0.0083        | 162.6667 | 1220 | 0.7118          | 0.9239   |
| 0.0           | 165.3333 | 1240 | 0.7793          | 0.9130   |
| 0.0           | 168.0    | 1260 | 0.7330          | 0.9239   |
| 0.0038        | 170.6667 | 1280 | 0.5990          | 0.9348   |
| 0.0001        | 173.3333 | 1300 | 0.6496          | 0.9239   |
| 0.0           | 176.0    | 1320 | 0.8535          | 0.8913   |
| 0.0           | 178.6667 | 1340 | 0.6108          | 0.9348   |
| 0.0           | 181.3333 | 1360 | 0.5813          | 0.9348   |
| 0.0           | 184.0    | 1380 | 0.5817          | 0.9239   |
| 0.0           | 186.6667 | 1400 | 0.5852          | 0.9239   |
| 0.0           | 189.3333 | 1420 | 0.5877          | 0.9239   |
| 0.0           | 192.0    | 1440 | 0.5941          | 0.9239   |
| 0.0           | 194.6667 | 1460 | 0.6219          | 0.9130   |
| 0.0           | 197.3333 | 1480 | 0.6350          | 0.9130   |
| 0.0           | 200.0    | 1500 | 0.6388          | 0.9130   |
| 0.0           | 202.6667 | 1520 | 0.6409          | 0.9130   |
| 0.0           | 205.3333 | 1540 | 0.6423          | 0.9130   |
| 0.0           | 208.0    | 1560 | 0.6430          | 0.9130   |
| 0.0           | 210.6667 | 1580 | 0.6336          | 0.9130   |
| 0.0           | 213.3333 | 1600 | 0.7124          | 0.9022   |
| 0.0           | 216.0    | 1620 | 0.7457          | 0.9022   |
| 0.0           | 218.6667 | 1640 | 0.7498          | 0.9022   |
| 0.0           | 221.3333 | 1660 | 0.7505          | 0.9022   |
| 0.0           | 224.0    | 1680 | 0.7512          | 0.9022   |
| 0.0           | 226.6667 | 1700 | 0.7660          | 0.9022   |
| 0.0           | 229.3333 | 1720 | 0.7682          | 0.9022   |
| 0.0           | 232.0    | 1740 | 0.7682          | 0.9022   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1