Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
animals
transfer-learning
vision-transformer
own-dataset
huggingface
Generated from Trainer
Eval Results (legacy)
Instructions to use Dalmatiner/cv_animals with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dalmatiner/cv_animals with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dalmatiner/cv_animals") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Dalmatiner/cv_animals") model = AutoModelForImageClassification.from_pretrained("Dalmatiner/cv_animals", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73f328d74df1df815b1efede77219483601dd633021ce38a84283e5402204a24
- Size of remote file:
- 5.37 kB
- SHA256:
- 02542a35597f69a75766f084b375dd83945042cf11ae6dbb02b237b212da3f83
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