Instructions to use rathi2023/owlvit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rathi2023/owlvit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="rathi2023/owlvit-base-patch32")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("rathi2023/owlvit-base-patch32") model = AutoModelForZeroShotObjectDetection.from_pretrained("rathi2023/owlvit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 400
Browse files
runs/Apr20_06-13-26_e8a9fe3c62aa/events.out.tfevents.1713593629.e8a9fe3c62aa.180.1
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 sha256:32ee2236ca074925a56ef89443a2cc8166817e0c57efc71522f01478d2b2f0b4
|
| 3 |
+
size 9391
|