Instructions to use gOLIVES/OLIVES_Dataset_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gOLIVES/OLIVES_Dataset_Models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gOLIVES/OLIVES_Dataset_Models") 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("gOLIVES/OLIVES_Dataset_Models") model = AutoModelForImageClassification.from_pretrained("gOLIVES/OLIVES_Dataset_Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 592 Bytes
5585e25 | 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 | {
"_name_or_path": "google/resnet18",
"architectures": [
"ResNetForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "No DR",
"1": "No DME"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"No DR": "0",
"No DME": "1"
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"transformers_version": "4.8.2"
}
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