Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use Kisu-2003/Plastic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kisu-2003/Plastic-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kisu-2003/Plastic-classification") 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("Kisu-2003/Plastic-classification") model = AutoModelForImageClassification.from_pretrained("Kisu-2003/Plastic-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Kisu-2003/Plastic-classification: direct link, hf CLI and curl.
- Browser
- Download file 836 Bytes
-
https://huggingface.co/Kisu-2003/Plastic-classification/resolve/main/README.md
- Command line
-
hf download hf://Kisu-2003/Plastic-classification/README.md
-
curl -L -o README.md https://huggingface.co/Kisu-2003/Plastic-classification/resolve/main/README.md
836 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Plastic-classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7032967209815979
Plastic-classification
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.




