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
| { | |
| "barn": { | |
| "mean_confidence": 0.8931, | |
| "std_confidence": 0.0913, | |
| "p5": 0.7926, | |
| "p95": 0.9542, | |
| "median": 0.914, | |
| "min": 0.2958, | |
| "n_samples": 168 | |
| }, | |
| "bridge": { | |
| "mean_confidence": 0.8854, | |
| "std_confidence": 0.0723, | |
| "p5": 0.7901, | |
| "p95": 0.9361, | |
| "median": 0.9015, | |
| "min": 0.4187, | |
| "n_samples": 168 | |
| }, | |
| "castle": { | |
| "mean_confidence": 0.8239, | |
| "std_confidence": 0.0551, | |
| "p5": 0.7466, | |
| "p95": 0.886, | |
| "median": 0.8323, | |
| "min": 0.5112, | |
| "n_samples": 168 | |
| }, | |
| "mosque": { | |
| "mean_confidence": 0.8542, | |
| "std_confidence": 0.0667, | |
| "p5": 0.7693, | |
| "p95": 0.9241, | |
| "median": 0.8651, | |
| "min": 0.402, | |
| "n_samples": 168 | |
| }, | |
| "skyscraper": { | |
| "mean_confidence": 0.889, | |
| "std_confidence": 0.0335, | |
| "p5": 0.8432, | |
| "p95": 0.9224, | |
| "median": 0.8946, | |
| "min": 0.683, | |
| "n_samples": 168 | |
| }, | |
| "stadium": { | |
| "mean_confidence": 0.8359, | |
| "std_confidence": 0.092, | |
| "p5": 0.679, | |
| "p95": 0.9061, | |
| "median": 0.8562, | |
| "min": 0.3142, | |
| "n_samples": 168 | |
| }, | |
| "temple": { | |
| "mean_confidence": 0.8204, | |
| "std_confidence": 0.1099, | |
| "p5": 0.4935, | |
| "p95": 0.8917, | |
| "median": 0.8527, | |
| "min": 0.265, | |
| "n_samples": 168 | |
| }, | |
| "windmill": { | |
| "mean_confidence": 0.8867, | |
| "std_confidence": 0.076, | |
| "p5": 0.7904, | |
| "p95": 0.9353, | |
| "median": 0.8998, | |
| "min": 0.2807, | |
| "n_samples": 168 | |
| } | |
| } |