Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use EdwarV/computer_vision_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdwarV/computer_vision_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="EdwarV/computer_vision_example") 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("EdwarV/computer_vision_example") model = AutoModelForImageClassification.from_pretrained("EdwarV/computer_vision_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3b3b2efc111a5de99f65ec8b67587d4d37c55404f560b88487fb57ed96269e8
- Size of remote file:
- 4.41 kB
- SHA256:
- 2c286318c58ec9592114f9b80359b4468b80645d8b1214856215350e1a894d39
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