Image Classification
Keras
LiteRT
TF-Keras
Safetensors
English
efficientnetv2-s
efficientnetv2
fgic
transfer-learning
gem-pooling
focal-loss
swa
grad-cam
calibration
temperature-scaling
computer-vision
tensorflow.js
Eval Results (legacy)
Instructions to use 0xgr3y/Arch-Building-Image-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use 0xgr3y/Arch-Building-Image-Classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://0xgr3y/Arch-Building-Image-Classification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "ece": 0.1204, | |
| "n_bins": 15, | |
| "bin_boundaries": [ | |
| 0.0, | |
| 0.0667, | |
| 0.1333, | |
| 0.2, | |
| 0.2667, | |
| 0.3333, | |
| 0.4, | |
| 0.4667, | |
| 0.5333, | |
| 0.6, | |
| 0.6667, | |
| 0.7333, | |
| 0.8, | |
| 0.8667, | |
| 0.9333, | |
| 1.0 | |
| ], | |
| "bin_accuracies": [ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.5, | |
| 0.5, | |
| 0.2, | |
| 0.5455, | |
| 1.0, | |
| 0.6923, | |
| 1.0, | |
| 0.9524, | |
| 0.9932, | |
| 0.9958, | |
| 1.0 | |
| ], | |
| "bin_confidences": [ | |
| 0.0333, | |
| 0.1, | |
| 0.1667, | |
| 0.265, | |
| 0.3007, | |
| 0.3677, | |
| 0.4269, | |
| 0.4925, | |
| 0.5783, | |
| 0.6356, | |
| 0.6943, | |
| 0.7747, | |
| 0.8415, | |
| 0.8981, | |
| 0.9446 | |
| ], | |
| "bin_counts": [ | |
| 0, | |
| 0, | |
| 0, | |
| 1, | |
| 4, | |
| 2, | |
| 10, | |
| 11, | |
| 2, | |
| 13, | |
| 16, | |
| 63, | |
| 439, | |
| 708, | |
| 75 | |
| ], | |
| "per_class_auc": { | |
| "barn": 0.995, | |
| "bridge": 0.9983, | |
| "castle": 0.9996, | |
| "mosque": 0.9987, | |
| "skyscraper": 0.9999, | |
| "stadium": 0.9999, | |
| "temple": 0.9976, | |
| "windmill": 0.9987 | |
| }, | |
| "temperature": 0.5400049194398104, | |
| "ece_before_t_scaling": 0.12037282419346627, | |
| "ece_after_t_scaling": 0.005275941378937221 | |
| } |