Instructions to use Dewa/Dog_Model_From_Scratch_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dewa/Dog_Model_From_Scratch_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dewa/Dog_Model_From_Scratch_v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import ClassificationModelForDogEmotion model = ClassificationModelForDogEmotion.from_pretrained("Dewa/Dog_Model_From_Scratch_v2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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metrics:
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- accuracy
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pipeline_tag: image-classification
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metrics:
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pipeline_tag: image-classification
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** Dewa Sahu using pytorch
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- **Model type:** Image Classification
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## Uses
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use for predicting the dogs emotion
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### Training Data
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Dewa/Dog_Emotion_Dataset_v2
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#### Hardware
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T4 GPU
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#### Software
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Google Colab used for training
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