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")# 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
- Xet hash:
- 78df5eb8f40c026792f2bf9ff48e5466444e58bb3f4de711b7d4adea1bdb2cbf
- Size of remote file:
- 3.38 kB
- SHA256:
- bfde58b2726b2f742b3a9e7d84dd685bb25d7c69dc8031471078d82fedf0797a
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