Image Classification
Transformers
PyTorch
TensorBoard
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
Instructions to use Woleek/day-night with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Woleek/day-night with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Woleek/day-night") 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("Woleek/day-night") model = AutoModelForImageClassification.from_pretrained("Woleek/day-night", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": { | |
| "height": 160, | |
| "width": 160 | |
| }, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "MobileNetV2FeatureExtractor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 192 | |
| } | |
| } | |