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README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- vision
+- fundus
+- glaucoma
+- REFUGE
+widget:
+- src: >-
+    https://huggingface.co/pamixsun/swinv2_tiny_for_glaucoma_classification/resolve/main/example.jpg
+  example_title: fundus image
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model utilizes a Swin Transformer architecture and has undergone supervised fine-tuning on retinal fundus images from the [REFUGE challenge dataset](https://refuge.grand-challenge.org/). 
+It is specialized in automated analysis of retinal fundus photographs for glaucoma detection. 
+By extracting hierarchical visual features from input fundus images in a cross-scale manner, the model is able to effectively categorize each image as either glaucoma or non-glaucoma. Extensive experiments demonstrate that this model architecture achieves state-of-the-art performance on the REFUGE benchmark for fundus image-based glaucoma classification. 
+To obtain optimal predictions, it is recommended to provide this model with standardized retinal fundus photographs captured using typical fundus imaging protocols.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [Xu Sun](https://pamixsun.github.io)
+- **Shared by:** [Xu Sun](https://pamixsun.github.io)
+- **Model type:** Image classification
+- **License:** Apache-2.0
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+The pretrained model provides glaucoma classification functionality solely based on analysis of retinal fundus images. 
+You may directly utilize the raw model without modification to categorize fundus images as either glaucoma or non-glaucoma. 
+This model is specialized in extracting discriminative features from fundus images to identify glaucoma manifestations. 
+However, to achieve optimal performance, it is highly recommended to fine-tune the model on a representative fundus image dataset prior to deployment in real-world applications.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+The model is specialized in analyzing retinal fundus images, and is trained exclusively on fundus image datasets to classify images as glaucoma or non-glaucoma. 
+Therefore, to obtain accurate predictions, it is crucial to only input fundus images when using this model. 
+Feeding other types of images may lead to meaningless results. 
+In summary, this model expects fundus images as input for glaucoma classification. 
+For the best performance, please adhere strictly to this input specification.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import cv2
+import torch
+
+from transformers import AutoImageProcessor, Swinv2ForImageClassification
+
+image = cv2.imread('./example.jpg')
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+processor = AutoImageProcessor.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification")
+model = Swinv2ForImageClassification.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification")
+
+inputs = processor(image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# model predicts either glaucoma or non-glaucoma.
+predicted_label = logits.argmax(-1).item()
+print(model.config.id2label[predicted_label])
+
+```
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+
+## Model Card Contact
+
+- [email protected]

config.json

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+{
+  "_name_or_path": "pamixsun/swinv2_tiny_for_glaucoma_classification",
+  "architectures": [
+    "Swinv2ForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    6,
+    2
+  ],
+  "drop_path_rate": 0.1,
+  "embed_dim": 96,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Non-glaucoma",
+    "1": "Glaucoma"
+  },
+  "image_size": 512,
+  "initializer_range": 0.02,
+  "label2id": {
+    "Glaucoma": "1",
+    "Non-glaucoma": "0"
+  },
+  "layer_norm_eps": 1e-05,
+  "mlp_ratio": 4.0,
+  "model_type": "swinv2",
+  "num_channels": 3,
+  "num_heads": [
+    3,
+    6,
+    12,
+    24
+  ],
+  "num_layers": 4,
+  "patch_size": 4,
+  "path_norm": true,
+  "pretrained_window_sizes": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.0",
+  "use_absolute_embeddings": false,
+  "window_size": 8
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 512,
+    "width": 512
+  }
+}

pytorch_model.bin

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