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README.md

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+---
+license: mit
+tags:
+- image-classification
+- medical
+- microscopy
+- sperm-analysis
+- pytorch
+library_name: pytorch
+---
+
+# Sperm Agglutinine Presence Detector
+
+## Model Description
+
+This model detects the presence of agglutinine in human sperm microscopic images using a ResNet50 architecture.
+
+**Architecture**: ResNet50 with custom classifier head  
+**Task**: Binary Image Classification  
+**Classes**: Negative, Positive  
+**Input**: Single frame, 800x600 RGB  
+**Framework**: PyTorch
+
+## Intended Use
+
+This model is designed for detecting agglutinine presence in human sperm microscopic images. It's intended for research and diagnostic support in reproductive medicine.
+
+## Model Details
+
+- **Input Format**: Single RGB image from microscopic video
+- **Preprocessing**: 
+  - Resize to 800x600
+  - ImageNet normalization
+- **Output**: Binary classification (Negative/Positive)
+
+## Architecture Details
+
+- **Backbone**: ResNet50 (pretrained on ImageNet)
+- **Classifier Head**: 
+  - Dropout(0.5)
+  - Linear(2048 → 512)
+  - ReLU
+  - Dropout(0.3)
+  - Linear(512 → 2)
+
+## Usage
+
+```python
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import numpy as np
+
+# Define model architecture
+model = models.resnet50(pretrained=False)
+num_ftrs = model.fc.in_features
+model.fc = nn.Sequential(
+    nn.Dropout(0.5),
+    nn.Linear(num_ftrs, 512),
+    nn.ReLU(),
+    nn.Dropout(0.3),
+    nn.Linear(512, 2)
+)
+
+# Load weights
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+checkpoint = torch.load('agglutinine_presence.pth', map_location=device)
+model.load_state_dict(checkpoint['model_state_dict'] if 'model_state_dict' in checkpoint else checkpoint)
+model.to(device)
+model.eval()
+
+# Define preprocessing
+transform = transforms.Compose([
+    transforms.Resize((600, 800)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load and preprocess image
+image = Image.open("path/to/image.jpg").convert('RGB')
+image_tensor = transform(image).unsqueeze(0).to(device)
+
+# Inference
+with torch.no_grad():
+    outputs = model(image_tensor)
+    probabilities = torch.softmax(outputs, dim=1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+    confidence = probabilities[0][predicted_class].item()
+
+class_names = ["Negative", "Positive"]
+print(f"Prediction: {class_names[predicted_class]}")
+print(f"Confidence: {confidence:.4f}")
+print(f"Positive probability: {probabilities[0][1].item():.4f}")
+```
+
+## Limitations
+
+- Trained on specific microscopy equipment and protocols
+- Performance may vary with different imaging conditions
+- Should be used as diagnostic support, not sole decision-making tool
+- Single-frame analysis may not capture temporal dynamics
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{sperm-agglutinine-detector,
+  author = {Raid Athmane Benlala},
+  title = {Sperm Agglutinine Presence Detector},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/raidAthmaneBenlala/agglutinine-detector}}
+}
+```