Instructions to use Dhika/raildefect6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect6") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Dhika/raildefect6") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 70
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 343278253
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c977c0e7c6fe31e76da75970e24f65f86b4be92eea820f50cfb3d2f928a05e79
|
| 3 |
size 343278253
|
runs/Jun13_11-33-41_e034bbdd7514/events.out.tfevents.1686656039.e034bbdd7514.8295.0
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 sha256:555d89a3ea0962d9b716f33bab0d28508d5c2a19eed2a7f8703ddb963e051c2f
|
| 3 |
+
size 7449
|