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": 0.7991, | |
| "std_confidence": 0.0637, | |
| "p5": 0.7336, | |
| "p95": 0.8487, | |
| "median": 0.81, | |
| "min": 0.3818, | |
| "n_samples": 168 | |
| }, | |
| "bridge": { | |
| "mean_confidence": 0.7987, | |
| "std_confidence": 0.0562, | |
| "p5": 0.7504, | |
| "p95": 0.8469, | |
| "median": 0.8079, | |
| "min": 0.4563, | |
| "n_samples": 168 | |
| }, | |
| "castle": { | |
| "mean_confidence": 0.8063, | |
| "std_confidence": 0.0711, | |
| "p5": 0.752, | |
| "p95": 0.8501, | |
| "median": 0.8206, | |
| "min": 0.3063, | |
| "n_samples": 168 | |
| }, | |
| "mosque": { | |
| "mean_confidence": 0.7954, | |
| "std_confidence": 0.0558, | |
| "p5": 0.7384, | |
| "p95": 0.8426, | |
| "median": 0.8057, | |
| "min": 0.3295, | |
| "n_samples": 168 | |
| }, | |
| "skyscraper": { | |
| "mean_confidence": 0.8199, | |
| "std_confidence": 0.0389, | |
| "p5": 0.7749, | |
| "p95": 0.865, | |
| "median": 0.8239, | |
| "min": 0.5285, | |
| "n_samples": 168 | |
| }, | |
| "stadium": { | |
| "mean_confidence": 0.782, | |
| "std_confidence": 0.0802, | |
| "p5": 0.6701, | |
| "p95": 0.8794, | |
| "median": 0.7924, | |
| "min": 0.2306, | |
| "n_samples": 168 | |
| }, | |
| "temple": { | |
| "mean_confidence": 0.7968, | |
| "std_confidence": 0.0675, | |
| "p5": 0.6844, | |
| "p95": 0.8474, | |
| "median": 0.8132, | |
| "min": 0.4061, | |
| "n_samples": 168 | |
| }, | |
| "windmill": { | |
| "mean_confidence": 0.8244, | |
| "std_confidence": 0.0443, | |
| "p5": 0.7649, | |
| "p95": 0.8724, | |
| "median": 0.8297, | |
| "min": 0.5435, | |
| "n_samples": 168 | |
| } | |
| } |