transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: gockle_v2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7843691148775894
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# gockle_v2

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: 0.9618
- Accuracy: 0.7844

## 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-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.7231        | 0.64  | 100   | 2.6467          | 0.2279   |
| 2.3217        | 1.28  | 200   | 2.4386          | 0.2288   |
| 2.0819        | 1.92  | 300   | 2.2887          | 0.2815   |
| 1.9583        | 2.56  | 400   | 2.1686          | 0.4501   |
| 1.8098        | 3.21  | 500   | 2.0731          | 0.5085   |
| 1.7511        | 3.85  | 600   | 1.9978          | 0.5320   |
| 1.6581        | 4.49  | 700   | 1.9233          | 0.5584   |
| 1.6094        | 5.13  | 800   | 1.8703          | 0.5706   |
| 1.5241        | 5.77  | 900   | 1.8192          | 0.6017   |
| 1.501         | 6.41  | 1000  | 1.7757          | 0.6111   |
| 1.4308        | 7.05  | 1100  | 1.7415          | 0.6281   |
| 1.3985        | 7.69  | 1200  | 1.7015          | 0.6375   |
| 1.3559        | 8.33  | 1300  | 1.6652          | 0.6403   |
| 1.3092        | 8.97  | 1400  | 1.6290          | 0.6488   |
| 1.3059        | 9.62  | 1500  | 1.6142          | 0.6620   |
| 1.2597        | 10.26 | 1600  | 1.5771          | 0.6704   |
| 1.2147        | 10.9  | 1700  | 1.5501          | 0.6902   |
| 1.1942        | 11.54 | 1800  | 1.5288          | 0.6911   |
| 1.1668        | 12.18 | 1900  | 1.5081          | 0.6902   |
| 1.1371        | 12.82 | 2000  | 1.4883          | 0.6949   |
| 1.1256        | 13.46 | 2100  | 1.4770          | 0.6930   |
| 1.0922        | 14.1  | 2200  | 1.4500          | 0.7081   |
| 1.0559        | 14.74 | 2300  | 1.4369          | 0.7072   |
| 1.054         | 15.38 | 2400  | 1.4157          | 0.7128   |
| 1.0465        | 16.03 | 2500  | 1.3899          | 0.7279   |
| 0.9965        | 16.67 | 2600  | 1.3734          | 0.7194   |
| 0.9876        | 17.31 | 2700  | 1.3603          | 0.7298   |
| 0.9791        | 17.95 | 2800  | 1.3422          | 0.7298   |
| 0.9551        | 18.59 | 2900  | 1.3309          | 0.7373   |
| 0.9313        | 19.23 | 3000  | 1.3223          | 0.7335   |
| 0.9211        | 19.87 | 3100  | 1.3052          | 0.7345   |
| 0.9071        | 20.51 | 3200  | 1.2897          | 0.7420   |
| 0.875         | 21.15 | 3300  | 1.2762          | 0.7561   |
| 0.8676        | 21.79 | 3400  | 1.2657          | 0.7542   |
| 0.8498        | 22.44 | 3500  | 1.2575          | 0.7580   |
| 0.8529        | 23.08 | 3600  | 1.2435          | 0.7542   |
| 0.8341        | 23.72 | 3700  | 1.2369          | 0.7561   |
| 0.8056        | 24.36 | 3800  | 1.2306          | 0.7533   |
| 0.8038        | 25.0  | 3900  | 1.2181          | 0.7665   |
| 0.7733        | 25.64 | 4000  | 1.2031          | 0.7655   |
| 0.7834        | 26.28 | 4100  | 1.2015          | 0.7637   |
| 0.7697        | 26.92 | 4200  | 1.1887          | 0.7637   |
| 0.7438        | 27.56 | 4300  | 1.1788          | 0.7674   |
| 0.733         | 28.21 | 4400  | 1.1740          | 0.7637   |
| 0.7244        | 28.85 | 4500  | 1.1671          | 0.7674   |
| 0.7091        | 29.49 | 4600  | 1.1563          | 0.7693   |
| 0.7138        | 30.13 | 4700  | 1.1543          | 0.7665   |
| 0.693         | 30.77 | 4800  | 1.1445          | 0.7665   |
| 0.6837        | 31.41 | 4900  | 1.1348          | 0.7731   |
| 0.6706        | 32.05 | 5000  | 1.1282          | 0.7702   |
| 0.6514        | 32.69 | 5100  | 1.1222          | 0.7712   |
| 0.6513        | 33.33 | 5200  | 1.1323          | 0.7665   |
| 0.6517        | 33.97 | 5300  | 1.1138          | 0.7693   |
| 0.637         | 34.62 | 5400  | 1.1014          | 0.7712   |
| 0.6277        | 35.26 | 5500  | 1.0949          | 0.7759   |
| 0.6103        | 35.9  | 5600  | 1.0882          | 0.7759   |
| 0.5916        | 36.54 | 5700  | 1.0888          | 0.7693   |
| 0.6101        | 37.18 | 5800  | 1.0890          | 0.7721   |
| 0.6042        | 37.82 | 5900  | 1.0779          | 0.7750   |
| 0.5618        | 38.46 | 6000  | 1.0769          | 0.7750   |
| 0.5878        | 39.1  | 6100  | 1.0638          | 0.7787   |
| 0.5522        | 39.74 | 6200  | 1.0611          | 0.7731   |
| 0.557         | 40.38 | 6300  | 1.0639          | 0.7768   |
| 0.5665        | 41.03 | 6400  | 1.0668          | 0.7740   |
| 0.5269        | 41.67 | 6500  | 1.0531          | 0.7759   |
| 0.5672        | 42.31 | 6600  | 1.0493          | 0.7759   |
| 0.5197        | 42.95 | 6700  | 1.0469          | 0.7759   |
| 0.5273        | 43.59 | 6800  | 1.0481          | 0.7740   |
| 0.5149        | 44.23 | 6900  | 1.0434          | 0.7712   |
| 0.5146        | 44.87 | 7000  | 1.0462          | 0.7787   |
| 0.5033        | 45.51 | 7100  | 1.0358          | 0.7759   |
| 0.5073        | 46.15 | 7200  | 1.0322          | 0.7806   |
| 0.4964        | 46.79 | 7300  | 1.0313          | 0.7815   |
| 0.4832        | 47.44 | 7400  | 1.0238          | 0.7797   |
| 0.484         | 48.08 | 7500  | 1.0355          | 0.7768   |
| 0.4856        | 48.72 | 7600  | 1.0263          | 0.7834   |
| 0.4688        | 49.36 | 7700  | 1.0178          | 0.7815   |
| 0.4628        | 50.0  | 7800  | 1.0161          | 0.7787   |
| 0.457         | 50.64 | 7900  | 1.0195          | 0.7768   |
| 0.4547        | 51.28 | 8000  | 1.0064          | 0.7825   |
| 0.4551        | 51.92 | 8100  | 1.0108          | 0.7806   |
| 0.4408        | 52.56 | 8200  | 1.0136          | 0.7768   |
| 0.4471        | 53.21 | 8300  | 1.0016          | 0.7834   |
| 0.4431        | 53.85 | 8400  | 1.0038          | 0.7863   |
| 0.4393        | 54.49 | 8500  | 1.0057          | 0.7815   |
| 0.4246        | 55.13 | 8600  | 0.9961          | 0.7797   |
| 0.4237        | 55.77 | 8700  | 1.0019          | 0.7806   |
| 0.4128        | 56.41 | 8800  | 0.9941          | 0.7806   |
| 0.4285        | 57.05 | 8900  | 0.9946          | 0.7815   |
| 0.4121        | 57.69 | 9000  | 0.9932          | 0.7806   |
| 0.4167        | 58.33 | 9100  | 0.9916          | 0.7825   |
| 0.4001        | 58.97 | 9200  | 0.9915          | 0.7825   |
| 0.4053        | 59.62 | 9300  | 0.9886          | 0.7815   |
| 0.3993        | 60.26 | 9400  | 0.9910          | 0.7844   |
| 0.3881        | 60.9  | 9500  | 0.9856          | 0.7863   |
| 0.3846        | 61.54 | 9600  | 0.9917          | 0.7806   |
| 0.3913        | 62.18 | 9700  | 0.9820          | 0.7834   |
| 0.3897        | 62.82 | 9800  | 0.9806          | 0.7844   |
| 0.3821        | 63.46 | 9900  | 0.9804          | 0.7825   |
| 0.3742        | 64.1  | 10000 | 0.9873          | 0.7844   |
| 0.3835        | 64.74 | 10100 | 0.9807          | 0.7834   |
| 0.3571        | 65.38 | 10200 | 0.9792          | 0.7844   |
| 0.38          | 66.03 | 10300 | 0.9786          | 0.7844   |
| 0.3612        | 66.67 | 10400 | 0.9769          | 0.7844   |
| 0.3628        | 67.31 | 10500 | 0.9991          | 0.7740   |
| 0.3655        | 67.95 | 10600 | 0.9737          | 0.7806   |
| 0.3489        | 68.59 | 10700 | 0.9745          | 0.7853   |
| 0.371         | 69.23 | 10800 | 0.9853          | 0.7787   |
| 0.3454        | 69.87 | 10900 | 0.9676          | 0.7825   |
| 0.3457        | 70.51 | 11000 | 0.9708          | 0.7853   |
| 0.3559        | 71.15 | 11100 | 0.9691          | 0.7863   |
| 0.3523        | 71.79 | 11200 | 0.9690          | 0.7872   |
| 0.3357        | 72.44 | 11300 | 0.9707          | 0.7815   |
| 0.344         | 73.08 | 11400 | 0.9690          | 0.7863   |
| 0.3527        | 73.72 | 11500 | 0.9788          | 0.7825   |
| 0.327         | 74.36 | 11600 | 0.9703          | 0.7825   |
| 0.3376        | 75.0  | 11700 | 0.9770          | 0.7787   |
| 0.3518        | 75.64 | 11800 | 0.9718          | 0.7834   |
| 0.3031        | 76.28 | 11900 | 0.9736          | 0.7863   |
| 0.3404        | 76.92 | 12000 | 0.9661          | 0.7825   |
| 0.3243        | 77.56 | 12100 | 0.9731          | 0.7853   |
| 0.3381        | 78.21 | 12200 | 0.9685          | 0.7900   |
| 0.3258        | 78.85 | 12300 | 0.9691          | 0.7844   |
| 0.3149        | 79.49 | 12400 | 0.9615          | 0.7844   |
| 0.3234        | 80.13 | 12500 | 0.9661          | 0.7853   |
| 0.3296        | 80.77 | 12600 | 0.9722          | 0.7815   |
| 0.3215        | 81.41 | 12700 | 0.9672          | 0.7834   |
| 0.3121        | 82.05 | 12800 | 0.9641          | 0.7834   |
| 0.3163        | 82.69 | 12900 | 0.9636          | 0.7834   |
| 0.3225        | 83.33 | 13000 | 0.9649          | 0.7853   |
| 0.3136        | 83.97 | 13100 | 0.9652          | 0.7825   |
| 0.3172        | 84.62 | 13200 | 0.9639          | 0.7853   |
| 0.3098        | 85.26 | 13300 | 0.9671          | 0.7834   |
| 0.3081        | 85.9  | 13400 | 0.9627          | 0.7806   |
| 0.3099        | 86.54 | 13500 | 0.9626          | 0.7815   |
| 0.3144        | 87.18 | 13600 | 0.9612          | 0.7815   |
| 0.2952        | 87.82 | 13700 | 0.9645          | 0.7863   |
| 0.3092        | 88.46 | 13800 | 0.9604          | 0.7853   |
| 0.3193        | 89.1  | 13900 | 0.9630          | 0.7844   |
| 0.3005        | 89.74 | 14000 | 0.9667          | 0.7815   |
| 0.2928        | 90.38 | 14100 | 0.9638          | 0.7844   |
| 0.315         | 91.03 | 14200 | 0.9644          | 0.7844   |
| 0.3095        | 91.67 | 14300 | 0.9637          | 0.7834   |
| 0.3036        | 92.31 | 14400 | 0.9615          | 0.7834   |
| 0.298         | 92.95 | 14500 | 0.9617          | 0.7844   |
| 0.2944        | 93.59 | 14600 | 0.9658          | 0.7834   |
| 0.3065        | 94.23 | 14700 | 0.9625          | 0.7834   |
| 0.2983        | 94.87 | 14800 | 0.9622          | 0.7844   |
| 0.2953        | 95.51 | 14900 | 0.9626          | 0.7834   |
| 0.3063        | 96.15 | 15000 | 0.9608          | 0.7853   |
| 0.3058        | 96.79 | 15100 | 0.9631          | 0.7853   |
| 0.2974        | 97.44 | 15200 | 0.9614          | 0.7844   |
| 0.3004        | 98.08 | 15300 | 0.9608          | 0.7844   |
| 0.3001        | 98.72 | 15400 | 0.9613          | 0.7853   |
| 0.2968        | 99.36 | 15500 | 0.9623          | 0.7853   |
| 0.2985        | 100.0 | 15600 | 0.9618          | 0.7844   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
