Instructions to use shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance")# Load model directly from transformers import AutoImageProcessor, VideoMask2FormerForVideoSegmentation processor = AutoImageProcessor.from_pretrained("shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance") model = VideoMask2FormerForVideoSegmentation.from_pretrained("shivalikasingh/video-mask2former-swin-base-IN21k-youtubevis-2021-instance") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +1 -1
preprocessor_config.json
CHANGED
|
@@ -9,7 +9,7 @@
|
|
| 9 |
0.4560000002384186,
|
| 10 |
0.4059999883174896
|
| 11 |
],
|
| 12 |
-
"image_processor_type": "
|
| 13 |
"image_std": [
|
| 14 |
0.2290000021457672,
|
| 15 |
0.2239999920129776,
|
|
|
|
| 9 |
0.4560000002384186,
|
| 10 |
0.4059999883174896
|
| 11 |
],
|
| 12 |
+
"image_processor_type": "VideoMask2FormerImageProcessor",
|
| 13 |
"image_std": [
|
| 14 |
0.2290000021457672,
|
| 15 |
0.2239999920129776,
|