Instructions to use Neto71/sea_mammals with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neto71/sea_mammals with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Neto71/sea_mammals") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Neto71/sea_mammals") model = AutoModelForImageClassification.from_pretrained("Neto71/sea_mammals", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Neto71/sea_mammals: direct link, hf CLI and curl.
- Browser
- Download file 803 Bytes
-
https://huggingface.co/Neto71/sea_mammals/resolve/main/README.md
- Command line
-
hf download hf://Neto71/sea_mammals/README.md
-
curl -L -o README.md https://huggingface.co/Neto71/sea_mammals/resolve/main/README.md
803 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: sea_mammals
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8472222089767456
sea_mammals
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.


