Image Classification
Transformers
Safetensors
English
vit
explicit-content-detection
mini
art
sensual-content-detection
Anime
Instructions to use prithivMLmods/vit-mini-explicit-content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/vit-mini-explicit-content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/vit-mini-explicit-content") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("prithivMLmods/vit-mini-explicit-content") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/vit-mini-explicit-content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
44cec11 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "ViTImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
}
}
|