Instructions to use neelimapreeti297/classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use neelimapreeti297/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://neelimapreeti297/classification") - Notebooks
- Google Colab
- Kaggle
Download config.json from neelimapreeti297/classification: direct link, hf CLI and curl.
- Browser
- Download file 486 Bytes
-
https://huggingface.co/neelimapreeti297/classification/resolve/main/config.json
- Command line
-
hf download hf://neelimapreeti297/classification/config.json
-
curl -L -o config.json https://huggingface.co/neelimapreeti297/classification/resolve/main/config.json
486 Bytes
| { | |
| "architectures": [ | |
| "CustomInceptionV3" | |
| ], | |
| "input_shape": [224, 224, 3], | |
| "weights": "imagenet", | |
| "include_top": false, | |
| "flatten": true, | |
| "dense_units": 3, | |
| "activation": "softmax", | |
| "train_datagen_params": { | |
| "rescale": 0.00392156862, | |
| "shear_range": 0.2, | |
| "zoom_range": 0.2, | |
| "horizontal_flip": true | |
| }, | |
| "test_datagen_params": { | |
| "rescale": 0.00392156862 | |
| }, | |
| "target_size": [224, 224], | |
| "batch_size": 32, | |
| "class_mode": "categorical" | |
| } | |