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")# pip install -U transformers accelerate # 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 5700
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 sha256:13e1abfea7f41ae277532ed2971988642dc4555af91e848870b9a009a3b1edf6
|
| 3 |
size 242883681
|
runs/Jun12_08-31-14_765124b5b6b2/events.out.tfevents.1686558761.765124b5b6b2.23.0
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:a360a7f86191a2c383ad542a928a4bcde651417e0f2b7cd1faf73438fa880a6e
|
| 3 |
+
size 22575
|