Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use goodcasper/exp_result1_resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use goodcasper/exp_result1_resnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="goodcasper/exp_result1_resnet") 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("goodcasper/exp_result1_resnet") model = AutoModelForImageClassification.from_pretrained("goodcasper/exp_result1_resnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b14a13c664ec2503d18a391742300faac322841fb30808ce8b8827daccbb2dd
- Size of remote file:
- 5.71 kB
- SHA256:
- dcde0a0772758320e1e70caa2e7368b83b180d20342102907563582708fa295d
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