Initial Commit :3

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README.md

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+---
+tags:
+- autotrain
+- vision
+- image-classification
+license: mit
+widget:
+- src: https://files.catbox.moe/72xdjy.png
+  example_title: Furry Avatar #1
+- src: https://files.catbox.moe/22bao8.jpg
+  example_title: Furry Avatar #2
+- src: https://files.catbox.moe/xahs5m.png
+  example_title: Normal Animal Avatar #1
+- src: https://files.catbox.moe/6zvcpu.png
+  example_title: Normal Animal Avatar #2
+- src: https://files.catbox.moe/gcltc9.png
+  example_title: Kemonomimi Avatar #1
+- src: https://files.catbox.moe/w4vcoc.png
+  example_title: Kemonomimi Avatar #2
+- src: https://files.catbox.moe/ujfzv0.png
+  example_title: Human Avatar #1
+- src: https://files.catbox.moe/yxx1qz.jpg
+  example_title: Human Avatar #2
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-1.jpg
+  example_title: Normal Cat :3
+co2_eq_emissions:
+  emissions: 2.8752228959859316
+---
+
+This detects furry images, mostly profile pictures, although it may be able detect any sort of furry picture (I haven't tried it, though).
+
+# Dataset Info
+
+This was trained on scraped pfp images from Mastodon, with some non-pfp images thrown in for "balancing" (i.e ensuring pokemon, kemonomimi (catgirls/foxgirls/etc), and normal animals weren't classified as 'furry')
+
+**Furry images**: 551  
+**Non-furry images**: 641  
+
+# Disclaimer
+
+Please do not ruin this by using this to harass anyone.  
+This is *not* intended to be used for targeted harrassement, and I will explicitly condemn any use that attempts to do so.
+
+If you're wondering why I made this public in the first place?  
+I believe in freedom of *information* - this image classification model has various perfectly valid uses, and it's kinda useless to keep it private.
+
+# Statistics
+
+## Model Trained Using AutoTrain
+
+- Problem type: Binary Classification
+- Model ID: 2890884434
+- CO2 Emissions (in grams): 2.8752
+
+## Validation Metrics
+
+- Loss: 0.175
+- Accuracy: 0.933
+- Precision: 0.938
+- Recall: 0.938
+- AUC: 0.975
+- F1: 0.938

config.json

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+{
+  "_name_or_path": "AutoTrain",
+  "architectures": [
+    "SwinForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    18,
+    2
+  ],
+  "drop_path_rate": 0.1,
+  "embed_dim": 128,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "bad",
+    "1": "good"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "bad": "0",
+    "good": "1"
+  },
+  "layer_norm_eps": 1e-05,
+  "max_length": 128,
+  "mlp_ratio": 4.0,
+  "model_type": "swin",
+  "num_channels": 3,
+  "num_heads": [
+    4,
+    8,
+    16,
+    32
+  ],
+  "num_layers": 4,
+  "padding": "max_length",
+  "patch_size": 4,
+  "path_norm": true,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "use_absolute_embeddings": false,
+  "window_size": 7
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

pytorch_model.bin

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