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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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- generated_from_trainer |
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datasets: |
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- cats_vs_dogs |
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metrics: |
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- accuracy |
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base_model: google/vit-base-patch16-224-in21k |
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model-index: |
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- name: vit-base-cats-vs-dogs |
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results: |
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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: cats_vs_dogs |
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type: cats_vs_dogs |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9883257403189066 |
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name: Accuracy |
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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-base-cats-vs-dogs |
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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 cats_vs_dogs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0369 |
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- Accuracy: 0.9883 |
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## how to use |
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```python |
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from transformers import ViTFeatureExtractor, ViTModel |
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from PIL import Image |
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import requests |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k') |
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model = ViTModel.from_pretrained('akahana/vit-base-cats-vs-dogs') |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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last_hidden_states = outputs.last_hidden_state |
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``` |
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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: 0.0002 |
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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: 1.0 |
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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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| 0.0949 | 1.0 | 2488 | 0.0369 | 0.9883 | |
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### Framework versions |
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- Transformers 4.12.5 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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