chulanpro5
commited on
Commit
•
4979959
1
Parent(s):
bad3d9d
Adding ONNX file of this model
Browse filesBeep boop I am the [ONNX export bot 🤖🏎️](https://huggingface.co/spaces/onnx/export). On behalf of [chulanpro5](https://huggingface.co/chulanpro5), I would like to add to this repository the model converted to ONNX.
What is ONNX? It stands for "Open Neural Network Exchange", and is the most commonly used open standard for machine learning interoperability. You can find out more at [onnx.ai](https://onnx.ai/)!
The exported ONNX model can be then be consumed by various backends as TensorRT or TVM, or simply be used in a few lines with 🤗 Optimum through ONNX Runtime, check out how [here](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models)!
- onnx/config.json +63 -0
- onnx/model.onnx +3 -0
- onnx/preprocessor_config.json +22 -0
onnx/config.json
ADDED
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{
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"_name_or_path": "Organika/sdxl-detector",
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"_num_labels": 2,
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"architectures": [
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"SwinForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"depths": [
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2,
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2,
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18,
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2
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],
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"drop_path_rate": 0.1,
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"embed_dim": 128,
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"encoder_stride": 32,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"id2label": {
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"0": "artificial",
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"1": "human"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"label2id": {
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"artificial": 0,
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"human": 1
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},
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"layer_norm_eps": 1e-05,
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"max_length": 128,
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"mlp_ratio": 4.0,
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"model_type": "swin",
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"num_channels": 3,
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"num_heads": [
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4,
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8,
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16,
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32
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],
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"num_layers": 4,
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"out_features": [
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"stage4"
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],
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"out_indices": [
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4
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],
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"padding": "max_length",
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"patch_size": 4,
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"path_norm": true,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"stage_names": [
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"stem",
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"stage1",
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"stage2",
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"stage3",
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"stage4"
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],
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"transformers_version": "4.37.2",
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"use_absolute_embeddings": false,
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"window_size": 7
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}
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onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:966487e0b8e43d35c803e7e6b794fb7164b1d9e23329388773a55f2b2bd4d1e4
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size 353735517
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onnx/preprocessor_config.json
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{
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ViTFeatureExtractor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 224,
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"width": 224
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}
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}
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