Image Classification
Transformers
PyTorch
Safetensors
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use varcoder/resnet-101-finetuned-CivilEng11k-newDS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/resnet-101-finetuned-CivilEng11k-newDS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="varcoder/resnet-101-finetuned-CivilEng11k-newDS") 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("varcoder/resnet-101-finetuned-CivilEng11k-newDS") model = AutoModelForImageClassification.from_pretrained("varcoder/resnet-101-finetuned-CivilEng11k-newDS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 697f9d5d61f3f89623d4ef48f5c6150986bcbeaa9bae3eb1e5776c9b8cfdab13
- Size of remote file:
- 4.27 kB
- SHA256:
- c0e570529d1a5177904b2d8702d149453e480d5c396997dcf4185acf7ce31def
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.