Instructions to use Dhika/raildefect8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dhika/raildefect8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dhika/raildefect8") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Dhika/raildefect8") model = AutoModelForImageClassification.from_pretrained("Dhika/raildefect8") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 420
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 sha256:9098f5f1b621dc9fe686f6e9921a73cc135948e9f9ba2d416a2e26ec40242cd5
|
| 3 |
size 343278253
|
runs/Jul02_08-40-56_d171167db7fd/events.out.tfevents.1688287272.d171167db7fd.11931.0
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 sha256:597b705d19e81e23d9b75880f88cf8f86f549882d719e7b844f3d6c3c2c1d911
|
| 3 |
+
size 24204
|