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commit files to HF hub

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README.md ADDED
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+ ---
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+ tags:
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+ - image-classification
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+ - pytorch
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+ - huggingpics
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+ metrics:
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+ - accuracy
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+
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+ model-index:
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+ - name: dog-food-swin-tiny-patch4-window7-224
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 1.0
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+ ---
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+
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+ # dog-food-swin-tiny-patch4-window7-224
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+
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+
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+ This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
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+ [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
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+
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+
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+
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+
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+ ## Example Images
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+
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+
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+ #### dog
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+
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+ ![dog](images/dog)
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+
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+ #### food
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+
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+ ![food](images/dog)
config.json ADDED
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+ {
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+ "_name_or_path": "microsoft/swin-tiny-patch4-window7-224",
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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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+ 6,
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+ 2
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+ ],
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+ "drop_path_rate": 0.1,
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+ "embed_dim": 96,
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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": 768,
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+ "id2label": {
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+ "0": "dog",
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+ "1": "food"
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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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+ "dog": "0",
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+ "food": "1"
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+ },
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+ "layer_norm_eps": 1e-05,
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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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+ 3,
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+ 6,
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+ 12,
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+ 24
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+ ],
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+ "num_layers": 4,
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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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+ "torch_dtype": "float32",
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+ "transformers_version": "4.20.0",
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+ "use_absolute_embeddings": false,
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+ "window_size": 7
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+ }
preprocessor_config.json ADDED
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+ {
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+ "do_normalize": true,
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+ "do_resize": true,
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+ "feature_extractor_type": "ViTFeatureExtractor",
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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_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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+ "size": 224
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+ }
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