Image Classification
Transformers
Safetensors
vit
image-detection
ai-image-generation
anime
ai-anime
human-detection
art
Instructions to use legekka/AI-Anime-Image-Detector-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use legekka/AI-Anime-Image-Detector-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="legekka/AI-Anime-Image-Detector-ViT") 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("legekka/AI-Anime-Image-Detector-ViT") model = AutoModelForImageClassification.from_pretrained("legekka/AI-Anime-Image-Detector-ViT") - Notebooks
- Google Colab
- Kaggle
File size: 726 Bytes
c727f46 | 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 | {
"_name_or_path": "checkpoints/pretrain2/",
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "real",
"1": "ai"
},
"image_size": 512,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"ai": 1,
"real": 0
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 32,
"problem_type": "single_label_classification",
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.43.3",
"use_cache": false
}
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