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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- f1 |
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model-index: |
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- name: Pokemon-classification-1stGen |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.9272453917274858 |
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--- |
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# Pokemon-classification-1stGen |
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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 [Dusduo/1stGen-Pokemon-Images](https://huggingface.co/datasets/Dusduo/1stGen-Pokemon-Images) dataset. |
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It has been trained to discriminate between the pokemons from the [1st Generation](https://en.wikipedia.org/wiki/List_of_generation_I_Pok%C3%A9mon). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4182 |
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- F1: 0.9272 |
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A demonstration of the model application is [hosted on Spaces](https://huggingface.co/spaces/Dusduo/GottaClassifyEmAll). |
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Feel free to check it out! |
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## Model description |
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Transformer-based vision model for pokemon image classification. |
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## Intended uses & limitations |
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This model is intended to classify between pokemons from the 1st Generation. Therefore, when provided with images of pokemon from posterior generation, the model outputs won't be usable as such. |
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Moreover, the model was not designed to handle non pokemon images as well as images presenting several entities. |
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However, an additional layer can help mitigate the risk of wrongly classifying non pokemon images by analyzing the spread of the output (the confusion of the model), such a layer can be found in my implementation, available [here](https://github.com/A-Duss/GottaClassifyEmAll). |
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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: 6.56462271373806e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 4.3698 | 1.0 | 527 | 3.2781 | 0.5784 | |
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| 2.3225 | 2.0 | 1055 | 1.6644 | 0.7368 | |
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| 1.1907 | 3.0 | 1582 | 0.9749 | 0.8475 | |
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| 0.6947 | 4.0 | 2110 | 0.6765 | 0.8939 | |
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| 0.4827 | 5.0 | 2637 | 0.5290 | 0.9171 | |
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| 0.3515 | 6.0 | 3165 | 0.4530 | 0.9195 | |
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| 0.3074 | 6.99 | 3689 | 0.4182 | 0.9272 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.2.0.dev20231126+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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