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README.md

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+---
+tags:
+  - image-classification
+  - bacteria
+  - microscopy
+  - transfer-learning
+  - efficientnet
+library_name: timm
+datasets:
+  - custom
+pipeline_tag: image-classification
+---
+
+# Bacteria Image Classifier (EfficientNet-B0)
+
+A fine-tuned EfficientNet-B0 model for classifying microscopy images of bacteria
+and fungi into 20 classes (10 organisms × 2 imaging types: gram stain and media plate).
+
+## Classes
+
+The model distinguishes between the following 20 classes:
+
+| Organism | Gram Stain | Media Plate |
+|----------|-----------|-------------|
+| Aspergillus niger | ✓ | ✓ |
+| Bacillus subtilis | ✓ | ✓ |
+| Candida albicans | ✓ | ✓ |
+| Clostridium sporogenes | ✓ | ✓ |
+| Enterococcus faecalis | ✓ | ✓ |
+| Escherichia coli | ✓ | ✓ |
+| Klebsiella pneumoniae | ✓ | ✓ |
+| Pseudomonas aeruginosa | ✓ | ✓ |
+| Staphylococcus aureus | ✓ | ✓ |
+| Streptococcus pyogenes | ✓ | ✓ |
+
+## Usage
+
+```python
+import timm, torch, json
+from torchvision import transforms
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+repo_id = "lederyou/bacteria-classifier"
+model_path = hf_hub_download(repo_id, "model.pth")
+class_names = json.load(open(hf_hub_download(repo_id, "class_names.json")))
+
+model = timm.create_model("efficientnet_b0", pretrained=False, num_classes=20)
+model.load_state_dict(torch.load(model_path, map_location="cpu"))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+img = transform(Image.open("your_image.png").convert("RGB")).unsqueeze(0)
+with torch.no_grad():
+    pred = model(img).argmax(1).item()
+print(class_names[pred])
+```
+
+## Training
+
+- **Base model**: EfficientNet-B0 (pretrained on ImageNet)
+- **Method**: Two-phase transfer learning (frozen backbone → full fine-tuning)
+- **Dataset**: 629 images, 20 classes, 70/15/15 train/val/test split