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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: rare-puppers
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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: 0.4895833432674408
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+ ---
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+
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+ # rare-puppers
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+
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+
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+ Autogenerated by HuggingPics🤗🖼️
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+
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+ Create your own image classifier for **anything** by running [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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+ Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
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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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+ #### algebra
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+
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+ ![algebra](images/algebra.jpg)
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+
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+ #### arithmetic
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+
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+ ![arithmetic](images/arithmetic.jpg)
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+
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+ #### calculus
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+
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+ ![calculus](images/calculus.jpg)
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+
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+ #### geometry
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+
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+ ![geometry](images/geometry.jpg)
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+
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+ #### trigonometry
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+
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+ ![trigonometry](images/trigonometry.jpg)
config.json ADDED
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+ {
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+ "_name_or_path": "google/vit-base-patch16-224-in21k",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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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": "algebra",
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+ "1": "arithmetic",
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+ "2": "calculus",
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+ "3": "geometry",
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+ "4": "trigonometry"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "algebra": "0",
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+ "arithmetic": "1",
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+ "calculus": "2",
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+ "geometry": "3",
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+ "trigonometry": "4"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.15.0"
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+ }
images/algebra.jpg ADDED
images/arithmetic.jpg ADDED
images/calculus.jpg ADDED
images/geometry.jpg ADDED
images/trigonometry.jpg ADDED
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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+ 0.5,
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+ 0.5
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+ ],
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+ "image_std": [
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+ 0.5,
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+ ],
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+ "resample": 2,
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+ "size": 224
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+ }
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