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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: creativeml-openrail-m | |
| datasets: | |
| - Dewa/Dog_Emotion_Dataset_v2 | |
| metrics: | |
| - accuracy | |
| pipeline_tag: image-classification | |
| # Model Card for Model ID | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| 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). | |
| ## Model Details | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** Dewa Sahu using pytorch | |
| - **Model type:** Image Classification | |
| ## Uses | |
| use for predicting the dogs emotion | |
| ### Training Data | |
| Dewa/Dog_Emotion_Dataset_v2 | |
| #### Hardware | |
| T4 GPU | |
| #### Software | |
| Google Colab used for training | |