Update README.md

README.md

@@ -2,4 +2,102 @@
 metrics:
 - accuracy
 pipeline_tag: image-classification
----
+base_model: vit-base-patch16-384
+model-index:
+- name: vit-base-nsfw-detector
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    metrics:
+    - type: accuracy
+      value: 0.9654
+      name: Accuracy
+    - type: AUC
+      value: 0.9948
+    - type: loss
+      value: 0.0937
+      name: Loss
+---
+
+# vit-base-nsfw-detector
+
+This model is a fine-tuned version of [vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on around 2000 images (drawings, photos...).
+It achieves the following results on the evaluation set:
+- Loss: 0.0937
+- Accuracy: 0.9654
+
+
+## Model description
+
+The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
+
+## Intended uses & limitations
+
+There are two classes: SFW and NSFW. The model has been trained to be restrictive and therefore classify "sexy" images as NSFW. That is, if the image shows cleavage or too much skin, it will be classified as NSFW. This is normal.
+
+Usage for a local image:
+```python
+from transformers import pipeline
+from PIL import Image
+
+img = Image.open("<path_to_image_file>")
+predict = pipeline("image-classification", model="AdamCodd/vit-base-nsfw-detector")
+predict(img)
+```
+
+Usage for a distant image:
+```python
+from transformers import ViTImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import requests
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
+model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+# Predicted class: sfw
+```
+
+The model has been trained on a variety of images (realistic, 3D, drawings), yet it is not perfect and some images may be wrongly classified as NSFW when they are not.
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- num_epochs: 1
+
+### Training results
+
+- Validation Loss: 0.0937
+- Accuracy: 0.9654, 
+- AUC: 0.9948
+
+Confusion Matrix:
+
+[1076   37]
+
+[  60 1627]
+
+### Framework versions
+
+- Transformers 4.36.2
+- Evaluate 0.4.1
+
+If you want to support me, you can [here](https://ko-fi.com/adamcodd).