jeffliu-LL
commited on
Update README.md
Browse files
README.md
CHANGED
@@ -11,9 +11,11 @@ tags:
|
|
11 |
- Aerial Imagery
|
12 |
- Disaster Response
|
13 |
- Emergency Management
|
|
|
|
|
14 |
---
|
15 |
# Model Card for MITLL/LADI-v2-classifier-small
|
16 |
-
LADI-v2-classifier-small is based on [google/bit-50](https://huggingface.co/google/bit-50) and fine-tuned on the LADI
|
17 |
|
18 |
π __NOTE__ π This model is the main version of the small model which is trained on all splits of the LADI v2 dataset. It is intended for deployment and fine-tuning purposes. If you are interested in reproducing the results of our paper, see the 'reference' versions of the classifiers [MITLL/LADI-v2-classifier-small-reference](https://huggingface.co/MITLL/LADI-v2-classifier-small-reference) and [MITLL/LADI-v2-classifier-large-reference](https://huggingface.co/MITLL/LADI-v2-classifier-large-reference) models, which are trained only on the training split of the dataset.
|
19 |
|
|
|
11 |
- Aerial Imagery
|
12 |
- Disaster Response
|
13 |
- Emergency Management
|
14 |
+
datasets:
|
15 |
+
- MITLL/LADI-v2-dataset
|
16 |
---
|
17 |
# Model Card for MITLL/LADI-v2-classifier-small
|
18 |
+
LADI-v2-classifier-small is based on [google/bit-50](https://huggingface.co/google/bit-50) and fine-tuned on the [MITLL/LADI-v2-dataset](https://huggingface.co/datasets/MITLL/LADI-v2-dataset). LADI-v2-classifier is trained to identify labels of interest to disaster response managers from aerial images.
|
19 |
|
20 |
π __NOTE__ π This model is the main version of the small model which is trained on all splits of the LADI v2 dataset. It is intended for deployment and fine-tuning purposes. If you are interested in reproducing the results of our paper, see the 'reference' versions of the classifiers [MITLL/LADI-v2-classifier-small-reference](https://huggingface.co/MITLL/LADI-v2-classifier-small-reference) and [MITLL/LADI-v2-classifier-large-reference](https://huggingface.co/MITLL/LADI-v2-classifier-large-reference) models, which are trained only on the training split of the dataset.
|
21 |
|