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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: swin-age-classifier
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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: 0.8100000023841858
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+ ---
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+
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+ # swin-age-classifier
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+
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+
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+ Trained on 40 epochs -
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+
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+ Data from: Ai Crowd - Blitz
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+ ai-blitz-xiii - Age Prediction
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+ https://www.aicrowd.com/challenges/ai-blitz-xiii/problems/age-prediction/
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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).
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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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "id2label": {
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+ "0": "0-10",
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+ "1": "10-20",
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+ "2": "20-30",
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+ "3": "30-40",
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+ "4": "40-50",
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+ "5": "50-60",
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+ "6": "60-70",
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+ "7": "70-80",
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+ "8": "80-90",
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+ "9": "90-100"
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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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+ "0-10": "0",
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+ "10-20": "1",
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+ "20-30": "2",
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+ "30-40": "3",
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+ "40-50": "4",
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+ "50-60": "5",
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+ "60-70": "6",
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+ "70-80": "7",
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+ "80-90": "8",
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+ "90-100": "9"
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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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+ "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": "4.16.2",
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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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