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pkr7098
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vit-cifar100-cifar100

Image Classification
Transformers
Safetensors
vit
vision
Generated from Trainer
Inference Endpoints
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  1. README.md +15 -16
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  ---
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- library_name: transformers
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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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- - image-classification
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- - vision
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- - generated_from_trainer
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  metrics:
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  - accuracy
 
 
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  model-index:
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  - name: vit-cifar100-cifar100
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  results: []
@@ -18,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-cifar100-cifar100
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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 cifar100 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2851
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- - Accuracy: 0.9252
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 1337
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 1.0634 | 1.0 | 5313 | 0.7235 | 0.8777 |
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- | 0.7839 | 2.0 | 10626 | 0.3731 | 0.9056 |
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- | 0.5749 | 3.0 | 15939 | 0.3214 | 0.9153 |
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- | 0.3432 | 4.0 | 21252 | 0.2990 | 0.9209 |
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- | 0.4763 | 5.0 | 26565 | 0.2851 | 0.9252 |
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  ### Framework versions
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  - Transformers 4.44.2
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  - Pytorch 2.0.1+cu117
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  - Datasets 3.0.0
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- - Tokenizers 0.19.1
 
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  ---
 
 
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  base_model: google/vit-base-patch16-224-in21k
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+ library_name: peft
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+ license: apache-2.0
 
 
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  metrics:
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  - accuracy
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+ tags:
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+ - generated_from_trainer
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  model-index:
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  - name: vit-cifar100-cifar100
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  results: []
 
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  # vit-cifar100-cifar100
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2550
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+ - Accuracy: 0.9236
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 1337
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.5206 | 1.0 | 5313 | 0.3323 | 0.9051 |
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+ | 0.7608 | 2.0 | 10626 | 0.2929 | 0.9139 |
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+ | 0.8691 | 3.0 | 15939 | 0.2725 | 0.9173 |
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+ | 0.3582 | 4.0 | 21252 | 0.2581 | 0.9232 |
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+ | 0.4711 | 5.0 | 26565 | 0.2550 | 0.9236 |
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  ### Framework versions
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+ - PEFT 0.12.0
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  - Transformers 4.44.2
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  - Pytorch 2.0.1+cu117
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  - Datasets 3.0.0
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+ - Tokenizers 0.19.1