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
metadata
license: creativeml-openrail-m
datasets:
- Dewa/Dog_Emotion_Dataset_v2
metrics:
- accuracy
pipeline_tag: image-classification
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
- 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