Instructions to use dexforint/train_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexforint/train_result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="dexforint/train_result")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("dexforint/train_result") model = AutoModelForObjectDetection.from_pretrained("dexforint/train_result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 38100
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 242883681
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7b317e854fb1301d1937afecbe71decd7858cc6c5fa169b716452e24d1fc526e
|
| 3 |
size 242883681
|
runs/Jun13_14-04-39_ce1a5a6bd9f2/events.out.tfevents.1686665177.ce1a5a6bd9f2.24.0
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 sha256:007ba1528b0a709931076849828496975eb06526ec1cf29b4eb1e96bee652e5a
|
| 3 |
+
size 125616
|