Image Classification
Transformers
Safetensors
swinv2
LADI
Aerial Imagery
Disaster Response
Emergency Management
Instructions to use MITLL/LADI-v2-classifier-large-reference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MITLL/LADI-v2-classifier-large-reference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MITLL/LADI-v2-classifier-large-reference") 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("MITLL/LADI-v2-classifier-large-reference") model = AutoModelForImageClassification.from_pretrained("MITLL/LADI-v2-classifier-large-reference") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,7 +20,7 @@ LADI-v2-classifier-large-reference is based on [microsoft/swinv2-large-patch4-wi
|
|
| 20 |
## Model Details
|
| 21 |
|
| 22 |
### Model Description
|
| 23 |
-
The model architecture is based on
|
| 24 |
|
| 25 |
- bridges_any
|
| 26 |
- buildings_any
|
|
|
|
| 20 |
## Model Details
|
| 21 |
|
| 22 |
### Model Description
|
| 23 |
+
The model architecture is based on swinv2 and fine-tuned on the LADI v2 dataset, which contains 10,000 aerial images labeled by volunteers from the Civil Air Patrol. The images are labeled using multi-label classification for the following classes:
|
| 24 |
|
| 25 |
- bridges_any
|
| 26 |
- buildings_any
|