Instructions to use defefekt/PDLO_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defefekt/PDLO_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="defefekt/PDLO_Classifier") 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("defefekt/PDLO_Classifier") model = AutoModelForImageClassification.from_pretrained("defefekt/PDLO_Classifier") - Notebooks
- Google Colab
- Kaggle
File size: 979 Bytes
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"architectures": [
"SwinForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"depths": [
2,
2,
18,
2
],
"drop_path_rate": 0.1,
"dtype": "float32",
"embed_dim": 192,
"encoder_stride": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1536,
"id2label": {
"0": "satisfactory",
"1": "unsatisfactory"
},
"image_size": 224,
"initializer_range": 0.02,
"label2id": {
"satisfactory": 0,
"unsatisfactory": 1
},
"layer_norm_eps": 1e-05,
"mlp_ratio": 4.0,
"model_type": "swin",
"num_channels": 3,
"num_heads": [
6,
12,
24,
48
],
"num_layers": 4,
"out_features": [
"stage4"
],
"out_indices": [
4
],
"patch_size": 4,
"path_norm": true,
"qkv_bias": true,
"stage_names": [
"stem",
"stage1",
"stage2",
"stage3",
"stage4"
],
"transformers_version": "5.0.0",
"use_absolute_embeddings": false,
"window_size": 7
}
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