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# dit-base-finetuned-rvlcdip-finetuned-data200

This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0080
- Accuracy: 0.5699

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1142        | 1.0   | 46   | 2.0131          | 0.3441   |
| 1.9953        | 2.0   | 92   | 1.9577          | 0.4086   |
| 1.9558        | 3.0   | 138  | 1.9231          | 0.4301   |
| 1.9251        | 4.0   | 184  | 1.8015          | 0.4946   |
| 1.6485        | 5.0   | 230  | 1.7045          | 0.5269   |
| 1.5973        | 6.0   | 276  | 1.5806          | 0.5054   |
| 1.4755        | 7.0   | 322  | 1.4849          | 0.5054   |
| 1.4537        | 8.0   | 368  | 1.4356          | 0.5161   |
| 1.416         | 9.0   | 414  | 1.4512          | 0.5269   |
| 1.3645        | 10.0  | 460  | 1.3857          | 0.5591   |
| 1.3017        | 11.0  | 506  | 1.3108          | 0.5484   |
| 1.2794        | 12.0  | 552  | 1.3027          | 0.5376   |
| 1.1553        | 13.0  | 598  | 1.2883          | 0.5484   |
| 1.1526        | 14.0  | 644  | 1.3554          | 0.5054   |
| 1.1116        | 15.0  | 690  | 1.3235          | 0.5914   |
| 1.1925        | 16.0  | 736  | 1.2401          | 0.5806   |
| 1.1297        | 17.0  | 782  | 1.3425          | 0.5914   |
| 0.9717        | 18.0  | 828  | 1.3538          | 0.5484   |
| 0.8404        | 19.0  | 874  | 1.2648          | 0.5699   |
| 0.7008        | 20.0  | 920  | 1.4971          | 0.5376   |
| 1.1454        | 21.0  | 966  | 1.4137          | 0.4839   |
| 0.6849        | 22.0  | 1012 | 1.2801          | 0.5591   |
| 0.8566        | 23.0  | 1058 | 1.2380          | 0.5699   |
| 0.8956        | 24.0  | 1104 | 1.2903          | 0.6129   |
| 0.8004        | 25.0  | 1150 | 1.4372          | 0.5591   |
| 0.818         | 26.0  | 1196 | 1.1640          | 0.6344   |
| 0.6387        | 27.0  | 1242 | 1.3120          | 0.6452   |
| 0.7282        | 28.0  | 1288 | 1.4678          | 0.5161   |
| 0.7426        | 29.0  | 1334 | 1.4815          | 0.5269   |
| 0.735         | 30.0  | 1380 | 1.2714          | 0.6129   |
| 0.6769        | 31.0  | 1426 | 1.2262          | 0.5699   |
| 0.5562        | 32.0  | 1472 | 1.3348          | 0.6344   |
| 0.6671        | 33.0  | 1518 | 1.4159          | 0.6129   |
| 0.3708        | 34.0  | 1564 | 1.6416          | 0.5484   |
| 0.3967        | 35.0  | 1610 | 1.3298          | 0.5699   |
| 0.4692        | 36.0  | 1656 | 1.3559          | 0.5699   |
| 0.632         | 37.0  | 1702 | 1.3349          | 0.5699   |
| 0.3719        | 38.0  | 1748 | 1.4697          | 0.5914   |
| 0.4238        | 39.0  | 1794 | 1.5207          | 0.6022   |
| 0.3608        | 40.0  | 1840 | 1.5557          | 0.5591   |
| 0.6252        | 41.0  | 1886 | 1.6247          | 0.5269   |
| 0.4183        | 42.0  | 1932 | 1.5885          | 0.5914   |
| 0.3922        | 43.0  | 1978 | 1.6593          | 0.5699   |
| 0.5715        | 44.0  | 2024 | 1.5270          | 0.5699   |
| 0.3656        | 45.0  | 2070 | 1.8899          | 0.5054   |
| 0.3656        | 46.0  | 2116 | 2.0936          | 0.4624   |
| 0.4003        | 47.0  | 2162 | 1.5610          | 0.5054   |
| 0.446         | 48.0  | 2208 | 1.7388          | 0.5376   |
| 0.5219        | 49.0  | 2254 | 1.4976          | 0.6129   |
| 0.3488        | 50.0  | 2300 | 1.5744          | 0.5914   |
| 0.323         | 51.0  | 2346 | 1.6312          | 0.6022   |
| 0.3713        | 52.0  | 2392 | 1.6975          | 0.5591   |
| 0.2981        | 53.0  | 2438 | 1.6229          | 0.5699   |
| 0.3422        | 54.0  | 2484 | 2.0909          | 0.4624   |
| 0.2538        | 55.0  | 2530 | 2.0966          | 0.5161   |
| 0.3868        | 56.0  | 2576 | 1.5614          | 0.6344   |
| 0.4662        | 57.0  | 2622 | 1.8929          | 0.5269   |
| 0.4277        | 58.0  | 2668 | 1.9573          | 0.5376   |
| 0.5301        | 59.0  | 2714 | 1.7999          | 0.5699   |
| 0.3867        | 60.0  | 2760 | 2.3481          | 0.4624   |
| 0.2334        | 61.0  | 2806 | 1.9924          | 0.5376   |
| 0.2921        | 62.0  | 2852 | 2.0454          | 0.5591   |
| 0.4386        | 63.0  | 2898 | 1.7798          | 0.5376   |
| 0.3299        | 64.0  | 2944 | 1.9370          | 0.5914   |
| 0.5982        | 65.0  | 2990 | 2.0527          | 0.5591   |
| 0.4433        | 66.0  | 3036 | 1.6222          | 0.6237   |
| 0.3717        | 67.0  | 3082 | 1.7977          | 0.5914   |
| 0.3642        | 68.0  | 3128 | 1.6988          | 0.5914   |
| 0.4541        | 69.0  | 3174 | 1.7567          | 0.6022   |
| 0.3464        | 70.0  | 3220 | 1.9029          | 0.5699   |
| 0.2764        | 71.0  | 3266 | 1.9611          | 0.6022   |
| 0.2138        | 72.0  | 3312 | 1.9333          | 0.5591   |
| 0.3928        | 73.0  | 3358 | 1.7701          | 0.5806   |
| 0.1811        | 74.0  | 3404 | 1.8330          | 0.5806   |
| 0.2076        | 75.0  | 3450 | 1.6676          | 0.6559   |
| 0.3326        | 76.0  | 3496 | 2.0036          | 0.6022   |
| 0.1343        | 77.0  | 3542 | 1.6937          | 0.6344   |
| 0.3031        | 78.0  | 3588 | 1.9223          | 0.6237   |
| 0.2743        | 79.0  | 3634 | 2.1681          | 0.5699   |
| 0.3392        | 80.0  | 3680 | 2.0505          | 0.6129   |
| 0.1346        | 81.0  | 3726 | 2.0190          | 0.5699   |
| 0.0652        | 82.0  | 3772 | 2.2910          | 0.5699   |
| 0.4219        | 83.0  | 3818 | 1.8858          | 0.5914   |
| 0.1386        | 84.0  | 3864 | 1.7976          | 0.6237   |
| 0.2155        | 85.0  | 3910 | 2.4278          | 0.5161   |
| 0.4901        | 86.0  | 3956 | 1.9239          | 0.6237   |
| 0.3141        | 87.0  | 4002 | 2.0954          | 0.6559   |
| 0.2328        | 88.0  | 4048 | 2.2602          | 0.5806   |
| 0.2768        | 89.0  | 4094 | 2.1083          | 0.5914   |
| 0.3476        | 90.0  | 4140 | 2.4922          | 0.5269   |
| 0.2029        | 91.0  | 4186 | 2.2094          | 0.5591   |
| 0.2421        | 92.0  | 4232 | 2.2407          | 0.5376   |
| 0.2034        | 93.0  | 4278 | 2.1488          | 0.5591   |
| 0.2461        | 94.0  | 4324 | 2.1332          | 0.5806   |
| 0.1462        | 95.0  | 4370 | 2.2702          | 0.5591   |
| 0.5213        | 96.0  | 4416 | 2.2134          | 0.5699   |
| 0.3634        | 97.0  | 4462 | 2.1066          | 0.5699   |
| 0.1698        | 98.0  | 4508 | 2.2736          | 0.6237   |
| 0.1685        | 99.0  | 4554 | 2.3919          | 0.5806   |
| 0.1971        | 100.0 | 4600 | 2.0664          | 0.6237   |
| 0.1496        | 101.0 | 4646 | 2.5661          | 0.5806   |
| 0.283         | 102.0 | 4692 | 2.0714          | 0.5699   |
| 0.185         | 103.0 | 4738 | 2.1369          | 0.6022   |
| 0.1489        | 104.0 | 4784 | 2.1653          | 0.6129   |
| 0.1231        | 105.0 | 4830 | 2.0890          | 0.6452   |
| 0.3224        | 106.0 | 4876 | 2.3771          | 0.5376   |
| 0.3452        | 107.0 | 4922 | 2.2537          | 0.6344   |
| 0.4404        | 108.0 | 4968 | 2.0253          | 0.6129   |
| 0.3408        | 109.0 | 5014 | 2.1653          | 0.5699   |
| 0.2406        | 110.0 | 5060 | 2.0196          | 0.6237   |
| 0.3051        | 111.0 | 5106 | 2.1980          | 0.6129   |
| 0.1515        | 112.0 | 5152 | 2.4104          | 0.5699   |
| 0.3836        | 113.0 | 5198 | 2.2342          | 0.6344   |
| 0.3572        | 114.0 | 5244 | 2.2321          | 0.6022   |
| 0.3006        | 115.0 | 5290 | 2.3555          | 0.5806   |
| 0.0965        | 116.0 | 5336 | 2.7237          | 0.4516   |
| 0.2023        | 117.0 | 5382 | 2.3798          | 0.6237   |
| 0.1272        | 118.0 | 5428 | 2.5357          | 0.5591   |
| 0.4318        | 119.0 | 5474 | 2.4913          | 0.5699   |
| 0.0414        | 120.0 | 5520 | 2.3760          | 0.6022   |
| 0.1785        | 121.0 | 5566 | 2.3920          | 0.6129   |
| 0.0142        | 122.0 | 5612 | 2.4256          | 0.6022   |
| 0.1262        | 123.0 | 5658 | 2.7212          | 0.5806   |
| 0.2219        | 124.0 | 5704 | 2.3683          | 0.5699   |
| 0.1629        | 125.0 | 5750 | 2.4280          | 0.5484   |
| 0.149         | 126.0 | 5796 | 3.0708          | 0.4839   |
| 0.2394        | 127.0 | 5842 | 2.2192          | 0.6022   |
| 0.2165        | 128.0 | 5888 | 2.4015          | 0.5806   |
| 0.0729        | 129.0 | 5934 | 2.2241          | 0.6022   |
| 0.2585        | 130.0 | 5980 | 2.9483          | 0.5054   |
| 0.1401        | 131.0 | 6026 | 2.3180          | 0.6129   |
| 0.4162        | 132.0 | 6072 | 3.0147          | 0.4946   |
| 0.1188        | 133.0 | 6118 | 2.3128          | 0.6237   |
| 0.0939        | 134.0 | 6164 | 2.5300          | 0.6022   |
| 0.1039        | 135.0 | 6210 | 2.5740          | 0.5699   |
| 0.3678        | 136.0 | 6256 | 2.5887          | 0.5914   |
| 0.3998        | 137.0 | 6302 | 2.5664          | 0.5376   |
| 0.1952        | 138.0 | 6348 | 2.1861          | 0.6774   |
| 0.2616        | 139.0 | 6394 | 2.7036          | 0.5806   |
| 0.2523        | 140.0 | 6440 | 2.5953          | 0.5806   |
| 0.2772        | 141.0 | 6486 | 2.4114          | 0.6129   |
| 0.2399        | 142.0 | 6532 | 2.3203          | 0.6237   |
| 0.3769        | 143.0 | 6578 | 2.7200          | 0.5591   |
| 0.0094        | 144.0 | 6624 | 2.7315          | 0.5591   |
| 0.1818        | 145.0 | 6670 | 2.5223          | 0.6129   |
| 0.3063        | 146.0 | 6716 | 2.3310          | 0.6237   |
| 0.222         | 147.0 | 6762 | 2.6180          | 0.5806   |
| 0.2505        | 148.0 | 6808 | 2.2976          | 0.6344   |
| 0.2705        | 149.0 | 6854 | 2.4091          | 0.5914   |
| 0.1624        | 150.0 | 6900 | 2.8030          | 0.5269   |
| 0.1322        | 151.0 | 6946 | 2.6379          | 0.5591   |
| 0.0876        | 152.0 | 6992 | 2.5781          | 0.5484   |
| 0.1332        | 153.0 | 7038 | 2.8476          | 0.5591   |
| 0.2727        | 154.0 | 7084 | 2.6779          | 0.5699   |
| 0.195         | 155.0 | 7130 | 3.0504          | 0.4839   |
| 0.152         | 156.0 | 7176 | 2.6103          | 0.5806   |
| 0.2811        | 157.0 | 7222 | 2.5947          | 0.6129   |
| 0.0742        | 158.0 | 7268 | 2.4666          | 0.6559   |
| 0.2052        | 159.0 | 7314 | 2.5116          | 0.5484   |
| 0.2598        | 160.0 | 7360 | 3.0400          | 0.5269   |
| 0.2846        | 161.0 | 7406 | 2.2042          | 0.6667   |
| 0.2653        | 162.0 | 7452 | 3.0598          | 0.5484   |
| 0.358         | 163.0 | 7498 | 2.7669          | 0.5806   |
| 0.0355        | 164.0 | 7544 | 2.4568          | 0.6237   |
| 0.1817        | 165.0 | 7590 | 2.9532          | 0.5806   |
| 0.0955        | 166.0 | 7636 | 2.4798          | 0.6237   |
| 0.1941        | 167.0 | 7682 | 2.7027          | 0.5699   |
| 0.1787        | 168.0 | 7728 | 2.4225          | 0.6237   |
| 0.0998        | 169.0 | 7774 | 2.5104          | 0.5914   |
| 0.0392        | 170.0 | 7820 | 2.6235          | 0.5806   |
| 0.2689        | 171.0 | 7866 | 2.9215          | 0.5806   |
| 0.0595        | 172.0 | 7912 | 2.8108          | 0.5699   |
| 0.148         | 173.0 | 7958 | 2.9213          | 0.5806   |
| 0.2159        | 174.0 | 8004 | 2.6172          | 0.6129   |
| 0.1221        | 175.0 | 8050 | 2.4386          | 0.6237   |
| 0.0691        | 176.0 | 8096 | 2.8642          | 0.5269   |
| 0.2014        | 177.0 | 8142 | 2.7364          | 0.6022   |
| 0.0379        | 178.0 | 8188 | 2.4859          | 0.6022   |
| 0.2202        | 179.0 | 8234 | 3.0665          | 0.5484   |
| 0.2078        | 180.0 | 8280 | 2.3521          | 0.6237   |
| 0.1051        | 181.0 | 8326 | 2.4827          | 0.6237   |
| 0.2257        | 182.0 | 8372 | 2.8155          | 0.5914   |
| 0.1339        | 183.0 | 8418 | 2.6274          | 0.6237   |
| 0.1414        | 184.0 | 8464 | 2.7645          | 0.5806   |
| 0.0993        | 185.0 | 8510 | 2.8886          | 0.5591   |
| 0.1769        | 186.0 | 8556 | 2.5164          | 0.6129   |
| 0.1575        | 187.0 | 8602 | 2.9346          | 0.5376   |
| 0.0251        | 188.0 | 8648 | 2.6099          | 0.5376   |
| 0.0536        | 189.0 | 8694 | 2.9630          | 0.5376   |
| 0.1748        | 190.0 | 8740 | 2.8360          | 0.5699   |
| 0.0151        | 191.0 | 8786 | 2.7525          | 0.6022   |
| 0.2198        | 192.0 | 8832 | 2.6656          | 0.5376   |
| 0.267         | 193.0 | 8878 | 3.0118          | 0.5591   |
| 0.1043        | 194.0 | 8924 | 3.0214          | 0.5699   |
| 0.0035        | 195.0 | 8970 | 2.7925          | 0.5806   |
| 0.0707        | 196.0 | 9016 | 2.7839          | 0.5806   |
| 0.0656        | 197.0 | 9062 | 3.0370          | 0.5376   |
| 0.1155        | 198.0 | 9108 | 2.6510          | 0.5914   |
| 0.1118        | 199.0 | 9154 | 2.7058          | 0.5699   |
| 0.3086        | 200.0 | 9200 | 3.0080          | 0.5699   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "dit-base-finetuned-rvlcdip-finetuned-data200", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5698924731182796, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard beit image-classification generated_from_trainer dataset:imagefolder model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us 

dit-base-finetuned-rvlcdip-finetuned-data200
============================================


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


* Loss: 3.0080
* Accuracy: 0.5699


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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #beit #image-classification #generated_from_trainer #dataset-imagefolder #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 200### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2