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
Download images/PVC.jpg from Kisu-2003/Plastic-classification: direct link, hf CLI and curl.
- Browser
- Download file 31.7 kB
-
https://huggingface.co/Kisu-2003/Plastic-classification/resolve/main/images/PVC.jpg
- Command line
-
hf download hf://Kisu-2003/Plastic-classification/images/PVC.jpg
-
curl -L -o PVC.jpg https://huggingface.co/Kisu-2003/Plastic-classification/resolve/main/images/PVC.jpg
31.7 kB

- Xet hash:
- 8cf43b1b7fff9e9340ec7f3d9133be7801b39fb460fa0e2760b205e8314b7c27
- Size of remote file:
- 31.7 kB
- SHA256:
- 357d1de84ac48f1a03bd322c6ad4d76a063e75881511a5188ff03980e06b7ae2
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