| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | - tr |
| | metrics: |
| | - accuracy |
| | base_model: |
| | - microsoft/resnet-50 |
| | pipeline_tag: image-classification |
| | library_name: keras |
| | tags: |
| | - image-classification |
| | - resnet50 |
| | - transfer-learning |
| | --- |
| | # ALSATIX ResNet50 Model |
| |
|
| | This model is trained to classify images into 5 categories: |
| |
|
| | 1. **Alkol**: Alcohol-related images |
| | 2. **Normal**: Regular images |
| | 3. **NSFW**: Not Safe for Work images |
| | 4. **Silah**: Weapon-related images |
| | 5. **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: |
| |
|
| | ```python |
| | 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) |
