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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: Light_Gender_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: 0.7785888314247131
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+ ---
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+
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+ # Light_Gender_Classifier
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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
config.json ADDED
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+ {
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+ "_name_or_path": "google/mobilenet_v2_1.0_224",
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+ "architectures": [
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+ "MobileNetV2ForImageClassification"
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+ ],
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+ "classifier_dropout_prob": 0.2,
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+ "depth_divisible_by": 8,
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+ "depth_multiplier": 1.0,
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+ "expand_ratio": 6,
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+ "finegrained_output": true,
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+ "first_layer_is_expansion": true,
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+ "hidden_act": "relu6",
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+ "id2label": {
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+ "0": "male",
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+ "1": "random things",
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+ "2": "random woman"
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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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+ "male": "0",
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+ "random things": "1",
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+ "random woman": "2"
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+ },
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+ "layer_norm_eps": 0.001,
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+ "min_depth": 8,
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+ "model_type": "mobilenet_v2",
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+ "num_channels": 3,
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+ "output_stride": 32,
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+ "problem_type": "single_label_classification",
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+ "semantic_loss_ignore_index": 255,
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+ "tf_padding": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.47.1"
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+ }
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+ size 9085340
preprocessor_config.json ADDED
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+ {
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+ "height": 224,
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+ "width": 224
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+ },
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+ "do_center_crop": true,
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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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+ 0.5,
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+ 0.5,
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+ 0.5
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+ ],
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+ "image_processor_type": "MobileNetV2FeatureExtractor",
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+ "image_std": [
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+ 0.5,
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+ 0.5,
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+ 0.5
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+ "resample": 2,
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+ "size": {
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+ }
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+ }
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