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--- |
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license: apache-2.0 |
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tags: |
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- huggingpics |
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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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model_index: |
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- name: planes-trains-automobiles |
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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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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9850746268656716 |
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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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# planes-trains-automobiles |
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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 huggingpics dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0534 |
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- Accuracy: 0.9851 |
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## Model description |
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Autogenerated by HuggingPics🤗🖼️ |
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Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). |
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Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). |
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## Example Images |
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#### automobiles |
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#### planes |
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#### trains |
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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: 2e-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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- 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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- 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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| 0.0283 | 1.0 | 48 | 0.0434 | 0.9851 | |
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| 0.0224 | 2.0 | 96 | 0.0548 | 0.9851 | |
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| 0.0203 | 3.0 | 144 | 0.0445 | 0.9851 | |
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| 0.0195 | 4.0 | 192 | 0.0534 | 0.9851 | |
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### Framework versions |
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- Transformers 4.9.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.3 |
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