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 120
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:b3eaaa7cb4576a1a9b25d7dede568d9dbc5b461cf9e0cf0b3c11dbf65713f391
|
| 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:b06667c37899c0e73a33d403f32e85b2bdc8a2ebc8810857bbb39637be64d64f
|
| 3 |
+
size 9804
|