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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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- vision |
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- generated_from_trainer |
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datasets: |
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- cifar10 |
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metrics: |
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- accuracy |
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model-index: |
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- name: cifar10_outputs |
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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: cifar10 |
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type: cifar10 |
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args: plain_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.991421568627451 |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: cifar10 |
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type: cifar10 |
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config: plain_text |
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split: test |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9674 |
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verified: true |
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- name: Precision Macro |
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type: precision |
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value: 0.9679512973887299 |
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verified: true |
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- name: Precision Micro |
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type: precision |
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value: 0.9674 |
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verified: true |
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- name: Precision Weighted |
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type: precision |
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value: 0.9679512973887299 |
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verified: true |
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- name: Recall Macro |
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type: recall |
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value: 0.9673999999999999 |
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verified: true |
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- name: Recall Micro |
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type: recall |
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value: 0.9674 |
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verified: true |
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- name: Recall Weighted |
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type: recall |
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value: 0.9674 |
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verified: true |
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- name: F1 Macro |
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type: f1 |
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value: 0.9674620969256708 |
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verified: true |
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- name: F1 Micro |
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type: f1 |
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value: 0.9674000000000001 |
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verified: true |
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- name: F1 Weighted |
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type: f1 |
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value: 0.967462096925671 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.1527363657951355 |
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verified: true |
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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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# cifar10_outputs |
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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 cifar10 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0806 |
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- Accuracy: 0.9914 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 0.0001 |
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- train_batch_size: 17 |
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- eval_batch_size: 17 |
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- seed: 1337 |
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- distributed_type: IPU |
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- gradient_accumulation_steps: 128 |
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- total_train_batch_size: 8704 |
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- total_eval_batch_size: 272 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.25 |
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- num_epochs: 100.0 |
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- training precision: Mixed Precision |
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### Training results |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cpu |
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- Datasets 2.3.3.dev0 |
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- Tokenizers 0.12.1 |
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