Instructions to use emergentai/cancer-efficientnetb7-undersampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use emergentai/cancer-efficientnetb7-undersampling with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://emergentai/cancer-efficientnetb7-undersampling") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -174,10 +174,10 @@ model = keras.models.load_model(model_path)
|
|
| 174 |
|
| 175 |
### Comparison of performance on Pre vs Post-stained images
|
| 176 |
|
| 177 |
-
| Comparison | Accuracy | F1-Score |
|
| 178 |
-
| ------------------------------ | -------- | -------- |
|
| 179 |
-
| Pre-stained Prediction | 0.6087 | 0.2703 |
|
| 180 |
-
| Post-stained Prediction | 0.7474 | 0.3441 |
|
| 181 |
|
| 182 |
---
|
| 183 |
|
|
|
|
| 174 |
|
| 175 |
### Comparison of performance on Pre vs Post-stained images
|
| 176 |
|
| 177 |
+
| Comparison | Accuracy | F1-Score | Precision |Recall |
|
| 178 |
+
| ------------------------------ | -------- | -------- | -------- | ------- |
|
| 179 |
+
| Pre-stained Prediction | 0.6087 | 0.2703 |0.1613 |0.8333|
|
| 180 |
+
| Post-stained Prediction | 0.7474 | 0.3441 |0.2222 |0.7619|
|
| 181 |
|
| 182 |
---
|
| 183 |
|