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
Download pytorch_model.bin from NTQAI/pedestrian_gender_recognition: direct link, hf CLI and curl.
- Browser
- Download file 347 MB
-
https://huggingface.co/NTQAI/pedestrian_gender_recognition/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NTQAI/pedestrian_gender_recognition/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NTQAI/pedestrian_gender_recognition/resolve/main/pytorch_model.bin
347 MB
- Xet hash:
- 6a71f77aa5c7d3ecde58f2cb717e25b5c74ea8f5d3fc255040bdc3bcb5b89681
- Size of remote file:
- 347 MB
- SHA256:
- 27019770f69fc40299f50bd2bd6ed78a143e68851e836fab4e465417617d6dd3
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