Instructions to use EverJun2/MLweek7_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EverJun2/MLweek7_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="EverJun2/MLweek7_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("EverJun2/MLweek7_1") model = AutoModelForObjectDetection.from_pretrained("EverJun2/MLweek7_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2200
Browse files
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 166497908
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e937a966e2f962af783ae639b5fbe9d0271b4c67d43146a221e23de3f9b82296
|
| 3 |
size 166497908
|
runs/Oct15_03-31-23_24fb95c1950b/events.out.tfevents.1760499347.24fb95c1950b.786.1
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 sha256:1ccff37ad13b371b105576d736542b230341a249952182c6f99d32e6577c9893
|
| 3 |
+
size 15076
|