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") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 205 Bytes
e34e0a8 | 1 2 3 4 5 6 7 8 | {
"epoch": 5.0,
"eval_accuracy": 0.9107332624867163,
"eval_loss": 0.21704278886318207,
"eval_runtime": 12.0095,
"eval_samples_per_second": 235.064,
"eval_steps_per_second": 29.393
} |