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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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widget: |
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- src: >- |
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https://datasets-server.huggingface.co/assets/keremberke/pokemon-classification/--/full/train/3/image/image.jpg |
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example_title: Abra |
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- src: >- |
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https://datasets-server.huggingface.co/cached-assets/keremberke/pokemon-classification/--/full/train/383/image/image.jpg |
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example_title: Blastoise |
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model-index: |
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- name: pokemon_classifier |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: keremberke/pokemon-classification |
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type: pokemon-classification |
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config: full |
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split: validation |
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args: full |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.08848920863309352 |
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datasets: |
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- keremberke/pokemon-classification |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# pokemon_classifier |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 8.0935 |
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- Accuracy: 0.0885 |
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## Model description |
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This model, referred to as "PokemonClassifier," is a fine-tuned version of google/vit-base-patch16-224 on Pokemon classification datasets. |
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Its primary objective is to accurately identify the Pokemon in input images. While this general summary provides information about its performance in terms |
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of loss and accuracy, its core function lies in precisely classifying Pokemon images. |
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## Intended uses & limitations |
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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, |
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Persian, Weepinbell, Golem, Dodrio, Raichu, Zapdos, Raticate, Magnemite, Ivysaur, Growlithe, Tangela, Drowzee, Rapidash, Venonat, Pidgeot, Nidorino, Porygon, |
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Lickitung, Rattata, Machop, Charmeleon, Slowbro, Parasect, Eevee, Starmie, Staryu, Psyduck, Dragonair, Magikarp, Vileplume, Marowak, Pidgeotto, Shellder, Mewtwo, |
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Farfetchd, Kingler, Seel, Kakuna, Doduo, Electabuzz, Charmander, Rhyhorn, Tauros, Dugtrio, Poliwrath, Gengar, Exeggutor, Dewgong, Jigglypuff, Geodude, Kadabra, Nidorina, |
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Sandshrew, Grimer, MrMime, Pidgey, Koffing, Ekans, Alolan Sandslash, Venusaur, Snorlax, Paras, Jynx, Chansey, Hitmonchan, Gastly, Kangaskhan, Oddish, Wigglytuff, |
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Graveler, Arcanine, Clefairy, Articuno, Poliwag, Abra, Squirtle, Voltorb, Ponyta, Moltres, Nidoqueen, Magmar, Onix, Vulpix, Butterfree, Krabby, Arbok, Clefable, Goldeen, |
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Magneton, Dratini, Caterpie, Jolteon, Nidoking, Alakazam, Dragonite, Fearow, Slowpoke, Weezing, Beedrill, Weedle, Cloyster, Vaporeon, Gyarados, Golduck, Machamp, Hitmonlee, |
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Primeape, Cubone, Sandslash, Scyther, Haunter, Metapod, Tentacruel, Aerodactyl, Kabutops, Ninetales, Zubat, Rhydon, Mew, Pinsir, Ditto, Victreebel, Omanyte, Horsea, Pikachu, |
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Blastoise, Venomoth, Charizard, Seadra, Muk, Spearow, Bulbasaur, Bellsprout, Electrode, Gloom, Poliwhirl, Flareon, Seaking, Hypno, Wartortle, Mankey, Tentacool, Exeggcute, |
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and Meowth. |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.0872 | 0.82 | 500 | 7.2669 | 0.0640 | |
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| 0.1581 | 1.64 | 1000 | 7.6072 | 0.0712 | |
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| 0.0536 | 2.46 | 1500 | 7.8952 | 0.0842 | |
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| 0.0169 | 3.28 | 2000 | 8.0935 | 0.0885 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
