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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://0xgr3y/Arch-Building-Image-Classification") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from 0xgr3y/Arch-Building-Image-Classification: direct link, hf CLI and curl.
- Browser
- Download file 641 Bytes
-
https://huggingface.co/0xgr3y/Arch-Building-Image-Classification/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://0xgr3y/Arch-Building-Image-Classification/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/0xgr3y/Arch-Building-Image-Classification/resolve/main/preprocessor_config.json
641 Bytes
| { | |
| "image_processing": { | |
| "size": { | |
| "width": 320, | |
| "height": 320 | |
| }, | |
| "resample": "bilinear", | |
| "normalize": true, | |
| "mode": "efficientnet_v2_preprocess_input", | |
| "channel_order": "RGB", | |
| "mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "scale": 1.0, | |
| "description": "preprocess_input is identity in TF 2.12+; EfficientNetV2-S includes internal Rescaling layer. Input expects raw [0, 255] float32." | |
| }, | |
| "input_name": "input_1", | |
| "output_name": "dense_1", | |
| "input_shape": [ | |
| 1, | |
| 320, | |
| 320, | |
| 3 | |
| ], | |
| "output_shape": [ | |
| 1, | |
| 8 | |
| ] | |
| } |