add .bin

README.md

@@ -1,28 +1,45 @@
-
 ---
 license: mit
 tags:
   - image-classification
   - resnet50
+  - medical
+  - acne-detection
 task:
   - image-classification
 output:
-  - label: "Class Name"
-    score: 0.95
+  - label: "level1"
+    score: 0.98
 widget:
-  - text: "path_to_image.jpg"
+  - text: "example_image.jpg"
     output:
-      - label: "Class Name"
-        score: 0.95
+      - label: "level3"
+        score: 0.85
 ---
-# Model Card for Your Model
 
-This is a pre-trained ResNet-50 model for image classification. It has been trained on [your dataset description].
+# ResNet-50 Model for Acne Severity Classification
+
+This is a fine-tuned ResNet-50 model designed to classify the severity of acne from medical images into five categories (Severity 1 to Severity 5). The model leverages transfer learning on ResNet-50 pre-trained on ImageNet and adapts it for acne severity classification tasks.
+
+## Model Details
+
+### Training Details
+- **Framework:** PyTorch
+- **Base Model:** ResNet-50 (pretrained on ImageNet)
+- **Dataset:** A balanced dataset of acne images annotated with severity levels (Severity 1 to 5).
+- **Preprocessing:** Images resized to 224x224 pixels, normalized using ImageNet statistics (mean: `[0.485, 0.456, 0.406]`, std: `[0.229, 0.224, 0.225]`).
+- **Optimizer:** Adam with a learning rate of 0.001.
+- **Loss Function:** CrossEntropyLoss.
+- **Epochs:** 10.
+- **Validation Accuracy:** 0.85 (on a held-out validation set).
+
+## Intended Use
+
+This model is intended for educational purposes and demonstrates image classification for medical images. It should not be used for clinical decision-making without further validation.
+
+## Example Usage
+
+You can use this model via the Hugging Face Transformers pipeline for inference. Ensure you have the `transformers` library installed:
 
-## Model Usage
-You can use this model with the Hugging Face API as follows:
-```python
-from transformers import pipeline
-classifier = pipeline("image-classification", model="username/model_name")
-result = classifier("path_to_image.jpg")
-print(result)
+```bash
+pip install transformers

best_resnet50_model.bin

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-size 94386402
+oid sha256:71a9075d20e585c4182626846ac0343ae23050bbf52be62b4d393ccc41ac5b24
+size 94379978

config.json

@@ -1,7 +1,7 @@
 {
-    "model_type": "resnet",
-    "architectures": ["ResNet"],
-    "num_labels": 4,
-    "image_size": 224,
-    "input_size": [3, 224, 224]
-  }
+  "num_labels": 4, 
+  "model_type": "resnet",
+  "architectures": ["ResNetForImageClassification"],
+  "hidden_size": 2048,
+  "labels": ["level0", "level1", "level2", "level3"]
+}