Duplicate from jiechau/adult-content-identify-image

Co-authored-by: Jie-Chau Huang <[email protected]>

.gitattributes

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README.md

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+---
+model-index:
+- name: adult-content-classifier-image
+  results: []
+pipeline_tag: image-classification
+tags:
+  - adult-content-classifier-image
+  - classifier
+  - adult
+  - adult-content
+  - image-classifier
+  - image
+  - classifier
+#widget:
+#  - text: "https://img.shopping.friday.tw/images/product/265/7951356/7951356_3_1.webp?172830"
+#    src: "https://img.shopping.friday.tw/images/product/265/7951356/7951356_3_1.webp?172830"
+#    example_title: "regular_一般商品圖片"
+#  - text: "https://img.shopping.friday.tw/images/product/259/7782883/7782883_3_1.webp?153546"
+#    src: "https://img.shopping.friday.tw/images/product/259/7782883/7782883_3_1.webp?153546"
+#    example_title: "adult_成人商品圖片"
+---
+
+# adult-content-identify-image
+
+(text version [here](https://huggingface.co/jiechau/adult-content-identify-text)  文字版本請參考 [這裡](https://huggingface.co/jiechau/adult-content-identify-text))
+
+
+Determine whether online sales products are adult content. Input: image content, Output results: 0 Unknown, 1 Adult Content, 2 General Merchandise.
+
+判斷網路銷售商品是否屬於成人內容。輸入圖片內容，輸出結果: 0 未知, 1 成人內容, 2 一般商品。
+
+# use transformers pipeline
+```python
+from transformers import pipeline, AutoConfig
+pipe = pipeline("image-classification", model="jiechau/adult-content-identify-image")
+config = AutoConfig.from_pretrained("jiechau/adult-content-identify-image")
+label2id = config.label2id
+id2label = config.id2label
+
+q = 'https://xxx.xxx.xxx/images/xxx/xxx.webp'
+q = 'https://xxx.xxx.xxx/images/xxx/xxx.jpg'
+result = pipe(q)
+print(result)
+print(label2id[result[0]['label']])
+# [{'label': 'adult_成人商品', 'score': 0.7516837120056152}, {'label': 'regular_一般商品', 'score': 0.2475457787513733}, {'label': 'unknown', 'score': 0.0007705678581260145}]
+# 1
+```

config.json

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+{
+  "_name_or_path": "jiechau/adult-content-identify-image",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "unknown",
+    "1": "adult_\u6210\u4eba\u5546\u54c1",
+    "2": "regular_\u4e00\u822c\u5546\u54c1"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "adult_\u6210\u4eba\u5546\u54c1": 1,
+    "regular_\u4e00\u822c\u5546\u54c1": 2,
+    "unknown": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "transformers_version": "4.37.1"
+}

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preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTFeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}