Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation") 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("hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation") model = AutoModelForImageClassification.from_pretrained("hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 326b9e7267dbde0925fc7015d16a79e25a8666d18b3721ed219f04bc6e590613
- Size of remote file:
- 5.24 kB
- SHA256:
- 769c963c04e15638f330903b013aba6483ec0fbb4dd4feebd5aa5d2bbeb434ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.