Image Classification
Transformers
PyTorch
ONNX
Safetensors
beit
vision
Generated from Trainer
Eval Results (legacy)
Instructions to use NTQAI/pedestrian_gender_recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTQAI/pedestrian_gender_recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="NTQAI/pedestrian_gender_recognition") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("NTQAI/pedestrian_gender_recognition") model = AutoModelForImageClassification.from_pretrained("NTQAI/pedestrian_gender_recognition", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Adding ONNX file of this model (#1)
- Adding ONNX file of this model (f528e4bc8eacb1e90c57ce58c13ba56994d29de7) Co-authored-by: Mathias Dall-Hansen <ivensamdh@users.noreply.huggingface.co>5bbae75 -
Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (46c391cce273597359b026b5aee54b2bb8abe3b4) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>5b58a22
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