Update README.md

README.md

@@ -1,8 +1,8 @@
-
 ---
 tags:
 - autotrain
 - image-classification
+- vision
 widget:
 - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
   example_title: Tiger
@@ -10,13 +10,47 @@ widget:
   example_title: Teapot
 - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
   example_title: Palace
+license: apache-2.0
+pipeline_tag: image-classification
 datasets:
 - Pranavkpba2000/skin_cancer_dataset
 ---
 
-# Model Trained Using AutoTrain
+# SkinCancer-Classifier(small-sized model)
+
+SkinCancer-Classifier is a fine-tuned version of [swin-base](https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k). It was introduced in this [paper](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in this [repository](https://github.com/microsoft/Swin-Transformer).
+It was fine tuned on this [dataset](https://huggingface.co/datasets/Pranavkpba2000/skin_cancer_dataset).
+
+
+## Model description
+
+The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png)
+
+[Source](https://paperswithcode.com/method/swin-transformer)
+
+
+### How to use
+
+Here is how to use this model to identify melanoma from a picture of a the affected area of the skin:
+
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import requests
+
+processor = AutoImageProcessor.from_pretrained("NeuronZero/SkinCancerClassifier")
+model = AutoModelForImageClassification.from_pretrained("NeuronZero/SkinCancerClassifier")
 
-- Problem type: Image Classification
+# Dataset url: https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic
+ 
+image_url = "https://storage.googleapis.com/kagglesdsdata/datasets/319080/643971/Skin%20cancer%20ISIC%20The%20International%20Skin%20Imaging%20Collaboration/Test/melanoma/ISIC_0000049.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240403%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240403T164047Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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"
+image = Image.open(requests.get(image_url, stream=True).raw)
 
-## Validation Metrics
-No validation metrics available
+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])
+```