Instructions to use varcoder/segformer-b4-crack-segmentation-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/segformer-b4-crack-segmentation-dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="varcoder/segformer-b4-crack-segmentation-dataset")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("varcoder/segformer-b4-crack-segmentation-dataset") model = SegformerForSemanticSegmentation.from_pretrained("varcoder/segformer-b4-crack-segmentation-dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2600
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14931789
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8bdc583be16a00058c761b3f1aa69f195fb87a41d9c40860fc1d494a776e3555
|
| 3 |
size 14931789
|
runs/Jul13_18-13-32_5e79d62eb25b/events.out.tfevents.1689272015.5e79d62eb25b.606.1
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cf1426ec825961732e2a9b3ba3ff6eef6d4627d24d3bdcdcac3e4e9be35d5ef0
|
| 3 |
+
size 429817
|