Update README.md

since there are some downloads, making it easier for new users

README.md

@@ -1,3 +1,49 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# About
+This is a fork of MichalMlodawski/nsfw-image-detection-large which became unavailable.
+
+# Usage example
+```
+from PIL import Image
+import torch
+from transformers import AutoProcessor, FocalNetForImageClassification
+
+DEVICE = torch.device("cuda")
+model_path = "lovetillion/nsfw-image-detection-large"
+
+# Load the model and feature extractor
+feature_extractor = AutoProcessor.from_pretrained(model_path)
+model = FocalNetForImageClassification.from_pretrained(model_path).to(DEVICE)
+model.eval()
+
+# Mapping from model labels to NSFW categories
+label_to_category = {
+    "LABEL_0": "Safe",
+    "LABEL_1": "Questionable",
+    "LABEL_2": "Unsafe"
+}
+
+filename = "example.png"
+image = Image.open(filename)
+inputs = feature_extractor(images=image, return_tensors="pt")
+inputs.to(DEVICE)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    confidence, predicted = torch.max(probabilities, 1)
+label = model.config.id2label[predicted.item()]
+
+if label != "SAFE":
+    print( label, confidence.item() * 100, filename )
+else:
+    print( label, confidence.item() * 100, filename )
+```
+
+# For more information
+
+* Live demonstration in a production ensemble workflow: https://piglet.video
+* Results from our ethical AI whitepaper: https://lovetillion.org/liaise.pdf
+* Join us on Telegram at https://t.me/pigletproject