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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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library_name: pytorch |
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tags: |
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- generated_from_trainer |
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datasets: |
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- VinayHajare/Fruits-30 |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-fruit-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: 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: Accuracy |
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type: accuracy |
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value: 0.9698795180722891 |
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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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# vit-fruit-classifier |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0194 |
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- Accuracy: 0.9699 |
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## Training and evaluation data |
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This model was fine-tuned on [the Fruits-30 dataset](https://huggingface.co/datasets/VinayHajare/Fruits-30), a collection of images featuring 30 different types of fruits. Each image has been preprocessed and standardized to a size of 224x224 pixels for uniformity. |
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### Dataset Composition |
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- Number of Classes: 30 |
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- Image Resolution: 224x224 pixels |
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- Total Images: 826 |
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### Training and Evaluation Split |
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The dataset was split into training and evaluation sets using dataset.train_test_split function with a 80/20 train-test split, resulting in: |
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- Training Set: 660 images |
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- Evaluation Set: 166 images |
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### Splitting Strategy |
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- The data was shuffled (shuffle=True) before splitting to ensure a random distribution of classes across the training and evaluation sets. |
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- Additionally, stratification was applied based on the "label" column (stratify_by_column='label') to maintain a balanced class distribution across both sets. This helps prevent the model from biasing towards classes with more samples in the training data. |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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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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| 2.668 | 2.38 | 100 | 2.0731 | 0.9217 | |
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| 1.6565 | 4.76 | 200 | 1.4216 | 0.9518 | |
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| 1.1627 | 7.14 | 300 | 1.1256 | 0.9578 | |
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| 0.9571 | 9.52 | 400 | 1.0224 | 0.9639 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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