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
File size: 641 Bytes
202ce04 c93fb53 202ce04 c93fb53 202ce04 c93fb53 202ce04 c93fb53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"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
]
} |