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
| timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region | |
| 2023-01-06T04:21:59,6f267fc3-7da5-4884-830d-71f0c8057ca4,codecarbon,1043.5702650547028,0.07675515188614081,0.10504157083518204,Vietnam,VNM,hanoi,N,, | |