Image Classification
Transformers
PyTorch
Safetensors
efficientnet
chest-xray
efficientnet-b0
medical-ai
radiology
deep-learning
Eval Results (legacy)
Instructions to use Dragonscypher/rayz_EfficientNet_B0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dragonscypher/rayz_EfficientNet_B0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dragonscypher/rayz_EfficientNet_B0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Dragonscypher/rayz_EfficientNet_B0") model = AutoModelForImageClassification.from_pretrained("Dragonscypher/rayz_EfficientNet_B0") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +3 -2
config.json
CHANGED
|
@@ -4,6 +4,7 @@
|
|
| 4 |
"depth_coefficient": 1.0,
|
| 5 |
"width_coefficient": 1.0,
|
| 6 |
"dropout_rate": 0.2,
|
| 7 |
-
"
|
| 8 |
-
"image_size": 224
|
|
|
|
| 9 |
}
|
|
|
|
| 4 |
"depth_coefficient": 1.0,
|
| 5 |
"width_coefficient": 1.0,
|
| 6 |
"dropout_rate": 0.2,
|
| 7 |
+
"num_labels": 1000,
|
| 8 |
+
"image_size": 224,
|
| 9 |
+
"framework": "pytorch"
|
| 10 |
}
|