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README.md

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+---
+license: mit
+tags:
+  - image-classification
+  - bacteria
+  - medical-imaging
+  - resnet
+  - pytorch
+  - dibas
+datasets:
+  - custom
+metrics:
+  - accuracy
+  - f1
+library_name: timm
+pipeline_tag: image-classification
+---
+
+# ResNet50 for Bacterial Colony Classification
+
+This model is a fine-tuned version of **ResNet50** on the [DIBaS (Digital Image of Bacterial Species)](http://misztal.edu.pl/software/databases/dibas/) dataset for classifying bacterial colony images into 33 species.
+
+## Model Description
+
+- **Model Architecture:** ResNet50 (pretrained on ImageNet)
+- **Task:** Multi-class image classification (33 bacterial species)
+- **Dataset:** DIBaS - 660+ microscopy images of bacterial colonies
+- **Framework:** PyTorch + timm
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| **Validation Accuracy** | 93.94% |
+| **Macro F1-Score** | 0.939 |
+| **Parameters** | 23.58M |
+| **Model Size** | 90.2 MB |
+| **GPU Latency** | 4.19 ms (RTX 4070 SUPER) |
+| **CPU Latency** | 44.86 ms |
+
+### Comparison with Other Models
+
+| Model | Params (M) | Val Accuracy |
+|-------|------------|--------------|
+| MobileNetV3-Large | 4.24 | **95.45%** |
+| **ResNet50** | 23.58 | 93.94% |
+| EfficientNet-B0 | 4.05 | 91.67% |
+
+## Training Details
+
+- **Optimizer:** AdamW (lr=1e-3, weight_decay=1e-4)
+- **Epochs:** 20
+- **Batch Size:** 24
+- **Image Size:** 224×224
+- **Augmentation:** RandomResizedCrop, HorizontalFlip
+- **Hardware:** NVIDIA RTX 4070 SUPER
+- **Mixed Precision:** Enabled (AMP)
+- **Train/Val/Test Split:** 70/20/10 (stratified, seed=42)
+
+## How to Use
+
+```python
+import timm
+import torch
+from PIL import Image
+from torchvision import transforms
+
+# Load model
+model = timm.create_model('resnet50', pretrained=False, num_classes=33)
+state_dict = torch.load('pytorch_model.bin', map_location='cpu')
+model.load_state_dict(state_dict)
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Inference
+image = Image.open('bacteria_image.jpg').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    outputs = model(input_tensor)
+    predicted_class = outputs.argmax(dim=1).item()
+
+print(f"Predicted class: {CLASS_NAMES[predicted_class]}")
+```
+
+### Class Labels
+
+```python
+CLASS_NAMES = [
+    "Acinetobacter_baumannii", "Actinomyces_israelii", "Bacteroides_fragilis",
+    "Bifidobacterium_spp", "Candida_albicans", "Clostridium_perfringens",
+    "Enterococcus_faecalis", "Enterococcus_faecium", "Escherichia_coli",
+    "Fusobacterium", "Lactobacillus_casei", "Lactobacillus_crispatus",
+    "Lactobacillus_delbrueckii", "Lactobacillus_gasseri", "Lactobacillus_jensenii",
+    "Lactobacillus_johnsonii", "Lactobacillus_paracasei", "Lactobacillus_plantarum",
+    "Lactobacillus_reuteri", "Lactobacillus_rhamnosus", "Lactobacillus_salivarius",
+    "Listeria_monocytogenes", "Micrococcus_spp", "Neisseria_gonorrhoeae",
+    "Porphyromonas_gingivalis", "Propionibacterium_acnes", "Proteus",
+    "Pseudomonas_aeruginosa", "Staphylococcus_aureus", "Staphylococcus_epidermidis",
+    "Staphylococcus_saprophyticus", "Streptococcus_agalactiae", "Veillonella"
+]
+```
+
+## Limitations
+
+- Trained on single laboratory/microscope setup (DIBaS dataset)
+- May not generalize to different imaging conditions
+- Not validated for clinical diagnostic use
+
+## Related Models
+
+- [MobileNetV3-Large](https://huggingface.co/ihoflaz/dibas-mobilenet-v3-large) - Best accuracy (95.45%), smaller model
+- [EfficientNet-B0](https://huggingface.co/ihoflaz/dibas-efficientnet-b0) - Lightweight alternative
+
+## Citation
+
+```bibtex
+@inproceedings{hoflaz2025bacterial,
+  title={Lightweight CNNs Outperform Vision Transformers for Bacterial Colony Classification},
+  author={Hoflaz, Ibrahim},
+  booktitle={IEEE Conference},
+  year={2025}
+}
+```
+
+## Resources
+
+- **GitHub:** [ihoflaz/bacterial-colony-classification](https://github.com/ihoflaz/bacterial-colony-classification)
+- **DIBaS Dataset:** [http://misztal.edu.pl/software/databases/dibas/](http://misztal.edu.pl/software/databases/dibas/)