Instructions to use hilmiatha/resnet18-flower-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hilmiatha/resnet18-flower-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hilmiatha/resnet18-flower-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hilmiatha/resnet18-flower-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
# ResNet18 Flower Classifier
|
| 2 |
This model classifies images into one of five flower types.
|
| 3 |
## Usage
|
|
|
|
| 1 |
+
---
|
| 2 |
+
datasets:
|
| 3 |
+
- miladfa7/5-Flower-Types-Classification-Dataset
|
| 4 |
+
language:
|
| 5 |
+
- id
|
| 6 |
+
metrics:
|
| 7 |
+
- accuracy
|
| 8 |
+
pipeline_tag: image-classification
|
| 9 |
+
tags:
|
| 10 |
+
- biology
|
| 11 |
+
---
|
| 12 |
+
metrics:
|
| 13 |
+
- name: Accuracy
|
| 14 |
+
type: Accuracy
|
| 15 |
+
value: 0.8980
|
| 16 |
+
|
| 17 |
# ResNet18 Flower Classifier
|
| 18 |
This model classifies images into one of five flower types.
|
| 19 |
## Usage
|