Instructions to use iammbrn/alsatix_image_control_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use iammbrn/alsatix_image_control_model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://iammbrn/alsatix_image_control_model") - Notebooks
- Google Colab
- Kaggle
ALSATIX ResNet50 Model
This model is trained to classify images into 5 categories:
- Alkol: Alcohol-related images
- Normal: Regular images
- NSFW: Not Safe for Work images
- Silah: Weapon-related images
- Tutun: Tobacco-related images
Model Architecture
- Base: ResNet50 pre-trained on ImageNet
- Custom top layers: Dense (256 units), Dropout (0.5), Output (5 classes)
Usage
To use this model for image classification:
from transformers import TFAutoModelForImageClassification, AutoImageProcessor
model = TFAutoModelForImageClassification.from_pretrained("iammbrn/alsatix_image_control_model")
processor = AutoImageProcessor.from_pretrained("iammbrn/alsatix_image_control_model")
# Preprocess your image
image = processor(image, return_tensors="pt")
predictions = model(**image)
- Downloads last month
- 8
Model tree for iammbrn/alsatix_image_control_model
Base model
microsoft/resnet-50