---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
widget:
- src: >-
    https://datasets-server.huggingface.co/assets/keremberke/pokemon-classification/--/full/train/3/image/image.jpg
  example_title: Abra
- src: >-
    https://datasets-server.huggingface.co/cached-assets/keremberke/pokemon-classification/--/full/train/383/image/image.jpg
  example_title: Blastoise
model-index:
- name: pokemon_classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: keremberke/pokemon-classification
      type: pokemon-classification
      config: full
      split: validation
      args: full
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.08848920863309352
datasets:
- keremberke/pokemon-classification
language:
- en
pipeline_tag: image-classification
---

<!-- 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. -->

# pokemon_classifier

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pokemon-classification and the full datasets.
It achieves the following results on the evaluation set:
- Loss: 8.0935
- Accuracy: 0.0885

## Model description

This model, referred to as "PokemonClassifier," is a fine-tuned version of google/vit-base-patch16-224 on Pokemon classification datasets.
Its primary objective is to accurately identify the Pokemon in input images. While this general summary provides information about its performance in terms
of loss and accuracy, its core function lies in precisely classifying Pokemon images.

## Intended uses & limitations

This model is limited to the training data it was exposed to and can only identify the following Pokémon: Golbat, Machoke, Omastar, Diglett, Lapras, Kabuto,
Persian, Weepinbell, Golem, Dodrio, Raichu, Zapdos, Raticate, Magnemite, Ivysaur, Growlithe, Tangela, Drowzee, Rapidash, Venonat, Pidgeot, Nidorino, Porygon,
Lickitung, Rattata, Machop, Charmeleon, Slowbro, Parasect, Eevee, Starmie, Staryu, Psyduck, Dragonair, Magikarp, Vileplume, Marowak, Pidgeotto, Shellder, Mewtwo,
Farfetchd, Kingler, Seel, Kakuna, Doduo, Electabuzz, Charmander, Rhyhorn, Tauros, Dugtrio, Poliwrath, Gengar, Exeggutor, Dewgong, Jigglypuff, Geodude, Kadabra, Nidorina,
Sandshrew, Grimer, MrMime, Pidgey, Koffing, Ekans, Alolan Sandslash, Venusaur, Snorlax, Paras, Jynx, Chansey, Hitmonchan, Gastly, Kangaskhan, Oddish, Wigglytuff,
Graveler, Arcanine, Clefairy, Articuno, Poliwag, Abra, Squirtle, Voltorb, Ponyta, Moltres, Nidoqueen, Magmar, Onix, Vulpix, Butterfree, Krabby, Arbok, Clefable, Goldeen,
Magneton, Dratini, Caterpie, Jolteon, Nidoking, Alakazam, Dragonite, Fearow, Slowpoke, Weezing, Beedrill, Weedle, Cloyster, Vaporeon, Gyarados, Golduck, Machamp, Hitmonlee,
Primeape, Cubone, Sandslash, Scyther, Haunter, Metapod, Tentacruel, Aerodactyl, Kabutops, Ninetales, Zubat, Rhydon, Mew, Pinsir, Ditto, Victreebel, Omanyte, Horsea, Pikachu,
Blastoise, Venomoth, Charizard, Seadra, Muk, Spearow, Bulbasaur, Bellsprout, Electrode, Gloom, Poliwhirl, Flareon, Seaking, Hypno, Wartortle, Mankey, Tentacool, Exeggcute,
and Meowth.

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0872        | 0.82  | 500  | 7.2669          | 0.0640   |
| 0.1581        | 1.64  | 1000 | 7.6072          | 0.0712   |
| 0.0536        | 2.46  | 1500 | 7.8952          | 0.0842   |
| 0.0169        | 3.28  | 2000 | 8.0935          | 0.0885   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3