Upload README.md

README.md

@@ -4,4 +4,58 @@ pipeline_tag: image-classification
 tags:
 - pytorch
 - vision
----
+---
+
+This model is the product of curiosity—imagine a choice that allows you to label anime images! 
+
+**Disclaimer**: The model has been trained on an entirely new dataset. Predictions made by the model *prior to 2023 might be off*. It's advisable to fine-tune the model according to your specific use case.
+
+# Quick setup guide:
+
+```python
+from transformers.modeling_outputs import ImageClassifierOutput
+from transformers import ViTImageProcessor, ViTForImageClassification
+import torch
+from PIL import Image
+
+model_name_or_path = "vit-anime-base/"
+processor = ViTImageProcessor.from_pretrained(model_name_or_path)
+model = ViTForImageClassification.from_pretrained(model_name_or_path)
+threshold = 0.3
+
+device = torch.device('cuda')
+
+image = Image.open(YOUR_IMAGE_PATH)
+
+inputs = processor(image, return_tensors='pt')
+
+model.to(device=device)
+model.eval()
+
+
+with torch.no_grad():
+    pixel_values = inputs['pixel_values'].to(device=device)
+
+    outputs : ImageClassifierOutput = model(pixel_values=pixel_values)
+
+    logits = outputs.logits  # The raw scores before applying any activation
+    sigmoid = torch.nn.Sigmoid()  # Sigmoid function to convert logits to probabilities
+    logits : torch.FloatTensor = sigmoid(logits)  # Applying sigmoid activation
+
+    predictions = []  # List to store predictions
+
+    for idx, p in enumerate(logits[0]):
+        if p > threshold:  # Applying a threshold of 0.3 to consider a class prediction
+            predictions.append((model.config.id2label[idx], p.item()))  # Storing class label and probability
+
+for tag in predictions:
+    print(tag)  
+
+
+```
+
+Why the `Sigmoid`?
+- Sigmoid turns boring scores into fun probabilities, so you can use thresholds and find more cool tags.
+- It's like a wizard turning regular stuff into magic potions!
+
+[Training guide](/training_guide.md)