
<!-- 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. -->

# vitLarge-16-384-2e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-large-patch16-384](https://huggingface.co/google/vit-large-patch16-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1519
- Accuracy: 0.9713

## 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: 0.0002
- 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7944        | 0.03  | 100  | 0.8483          | 0.7428   |
| 1.1261        | 0.07  | 200  | 1.0595          | 0.6911   |
| 0.7575        | 0.1   | 300  | 0.5007          | 0.8534   |
| 0.3567        | 0.14  | 400  | 0.5404          | 0.8391   |
| 0.4062        | 0.17  | 500  | 0.7795          | 0.7974   |
| 0.4227        | 0.21  | 600  | 0.3598          | 0.8851   |
| 0.3436        | 0.24  | 700  | 0.4550          | 0.8693   |
| 0.7695        | 0.28  | 800  | 0.5748          | 0.8247   |
| 0.2864        | 0.31  | 900  | 0.4017          | 0.8793   |
| 0.3718        | 0.35  | 1000 | 0.5384          | 0.8420   |
| 0.2764        | 0.38  | 1100 | 0.4682          | 0.8764   |
| 0.3438        | 0.42  | 1200 | 0.4194          | 0.8807   |
| 0.4031        | 0.45  | 1300 | 0.4105          | 0.8922   |
| 0.449         | 0.49  | 1400 | 0.4499          | 0.8678   |
| 0.2249        | 0.52  | 1500 | 0.2701          | 0.9066   |
| 0.2398        | 0.56  | 1600 | 0.4124          | 0.8807   |
| 0.5759        | 0.59  | 1700 | 0.8378          | 0.7960   |
| 0.1315        | 0.63  | 1800 | 0.4757          | 0.8779   |
| 0.4481        | 0.66  | 1900 | 0.3463          | 0.9037   |
| 0.2183        | 0.7   | 2000 | 0.4291          | 0.8779   |
| 0.2101        | 0.73  | 2100 | 0.3318          | 0.9109   |
| 1.0071        | 0.77  | 2200 | 2.9399          | 0.2098   |
| 0.3426        | 0.8   | 2300 | 0.4231          | 0.9023   |
| 0.1126        | 0.84  | 2400 | 0.3609          | 0.9124   |
| 0.3954        | 0.87  | 2500 | 0.4471          | 0.8994   |
| 0.2099        | 0.91  | 2600 | 0.3465          | 0.9052   |
| 0.1982        | 0.94  | 2700 | 0.4135          | 0.8994   |
| 0.1931        | 0.98  | 2800 | 0.3306          | 0.9095   |
| 0.1721        | 1.01  | 2900 | 0.3470          | 0.9195   |
| 0.1864        | 1.04  | 3000 | 0.3814          | 0.9124   |
| 0.0652        | 1.08  | 3100 | 0.2534          | 0.9296   |
| 0.1176        | 1.11  | 3200 | 0.2744          | 0.9210   |
| 0.0988        | 1.15  | 3300 | 0.2966          | 0.9325   |
| 0.0289        | 1.18  | 3400 | 0.2021          | 0.9555   |
| 0.1465        | 1.22  | 3500 | 0.1566          | 0.9583   |
| 0.2023        | 1.25  | 3600 | 0.2803          | 0.9353   |
| 0.1042        | 1.29  | 3700 | 0.2893          | 0.9282   |
| 0.1403        | 1.32  | 3800 | 0.3145          | 0.9239   |
| 0.0786        | 1.36  | 3900 | 0.3188          | 0.9267   |
| 0.2427        | 1.39  | 4000 | 0.6615          | 0.8693   |
| 0.3187        | 1.43  | 4100 | 0.3598          | 0.9195   |
| 0.0897        | 1.46  | 4200 | 0.2778          | 0.9425   |
| 0.068         | 1.5   | 4300 | 0.3445          | 0.9124   |
| 0.2165        | 1.53  | 4400 | 0.2351          | 0.9468   |
| 0.0807        | 1.57  | 4500 | 0.3111          | 0.9310   |
| 0.007         | 1.6   | 4600 | 0.2208          | 0.9483   |
| 0.0017        | 1.64  | 4700 | 0.1943          | 0.9411   |
| 0.081         | 1.67  | 4800 | 0.3503          | 0.9239   |
| 0.0285        | 1.71  | 4900 | 0.3109          | 0.9239   |
| 0.0495        | 1.74  | 5000 | 0.1233          | 0.9641   |
| 0.0201        | 1.78  | 5100 | 0.2508          | 0.9483   |
| 0.1186        | 1.81  | 5200 | 0.3854          | 0.9210   |
| 0.0283        | 1.85  | 5300 | 0.2336          | 0.9425   |
| 0.0569        | 1.88  | 5400 | 0.2872          | 0.9425   |
| 0.0498        | 1.92  | 5500 | 0.2462          | 0.9569   |
| 0.0101        | 1.95  | 5600 | 0.2256          | 0.9511   |
| 0.0474        | 1.99  | 5700 | 0.2201          | 0.9569   |
| 0.0008        | 2.02  | 5800 | 0.2079          | 0.9526   |
| 0.0           | 2.06  | 5900 | 0.1951          | 0.9583   |
| 0.0007        | 2.09  | 6000 | 0.1449          | 0.9626   |
| 0.003         | 2.12  | 6100 | 0.1411          | 0.9670   |
| 0.0028        | 2.16  | 6200 | 0.1889          | 0.9598   |
| 0.0018        | 2.19  | 6300 | 0.2356          | 0.9511   |
| 0.0087        | 2.23  | 6400 | 0.2185          | 0.9569   |
| 0.0169        | 2.26  | 6500 | 0.1898          | 0.9583   |
| 0.0003        | 2.3   | 6600 | 0.1879          | 0.9655   |
| 0.0008        | 2.33  | 6700 | 0.1331          | 0.9713   |
| 0.0001        | 2.37  | 6800 | 0.1537          | 0.9655   |
| 0.0002        | 2.4   | 6900 | 0.2148          | 0.9598   |
| 0.0079        | 2.44  | 7000 | 0.1258          | 0.9698   |
| 0.0004        | 2.47  | 7100 | 0.1557          | 0.9698   |
| 0.0           | 2.51  | 7200 | 0.1376          | 0.9698   |
| 0.0007        | 2.54  | 7300 | 0.1238          | 0.9713   |
| 0.0           | 2.58  | 7400 | 0.1433          | 0.9670   |
| 0.0023        | 2.61  | 7500 | 0.1537          | 0.9684   |
| 0.0004        | 2.65  | 7600 | 0.1302          | 0.9727   |
| 0.0002        | 2.68  | 7700 | 0.1557          | 0.9698   |
| 0.0013        | 2.72  | 7800 | 0.1614          | 0.9698   |
| 0.0           | 2.75  | 7900 | 0.1713          | 0.9670   |
| 0.0           | 2.79  | 8000 | 0.1458          | 0.9698   |
| 0.0           | 2.82  | 8100 | 0.1453          | 0.9698   |
| 0.0           | 2.86  | 8200 | 0.1527          | 0.9670   |
| 0.0           | 2.89  | 8300 | 0.1508          | 0.9698   |
| 0.0001        | 2.93  | 8400 | 0.1544          | 0.9713   |
| 0.0           | 2.96  | 8500 | 0.1506          | 0.9713   |
| 0.0           | 3.0   | 8600 | 0.1519          | 0.9713   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2

bhapa pitha(ভাপা পিঠা) biriyani(বিরিয়ানি) khichuri(খিচুড়ি) malpua pitha(মালপুয়া পিঠা) mustard hilsa(সরষে ইলিশ) nakshi pitha(নকশি পিঠা) panta ilish(পান্তা ইলিশ) patishapta pitha(পাটিসাপটা) prawn malai curry(চিংড়ি মালাইকারী) rasgulla(রসগোল্লা) rose cookies(ফুলঝুরি পিঠা) roshmalai(রসমালাই) chicken pulao(মোরগ পোলাও) shahi tukra(শাহি টুকরা) shingara(সিঙ্গারা) sweet yogurt(মিষ্টি দই) tehari(তেহারি) chickpease bhuna(ছোলাভুনা) egg curry(ডিমভুনা) falooda(ফালুদা) fuchka(ফুচকা) haleem(হালিম) jalebi(জিলাপী) kala bhuna(কালা ভুনা)