Image Classification
Transformers
Safetensors
efficientnet
Generated from Trainer
Eval Results (legacy)
Instructions to use aaa12963337/msi-efficientnet-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaa12963337/msi-efficientnet-pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aaa12963337/msi-efficientnet-pretrain") 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("aaa12963337/msi-efficientnet-pretrain") model = AutoModelForImageClassification.from_pretrained("aaa12963337/msi-efficientnet-pretrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8557d6e841a4e9f425b303d5de6e2ec2496c6a9de367ec77bfaad95b62959975
- Size of remote file:
- 4.28 kB
- SHA256:
- 2c40cc08598d511bfa9a746c433342d2646750ff66e40821dd1bc8c038a6e81a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.