Instructions to use beingbatman/5c_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beingbatman/5c_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="beingbatman/5c_2")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("beingbatman/5c_2") model = AutoModelForVideoClassification.from_pretrained("beingbatman/5c_2") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- all_results.json +8 -0
- trainer_state.json +0 -0
- val_results.json +8 -0
all_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 97.00891304347826,
|
| 3 |
+
"eval_accuracy": 0.44,
|
| 4 |
+
"eval_loss": 3.826441764831543,
|
| 5 |
+
"eval_runtime": 7.003,
|
| 6 |
+
"eval_samples_per_second": 3.57,
|
| 7 |
+
"eval_steps_per_second": 0.714
|
| 8 |
+
}
|
trainer_state.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
val_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 97.00891304347826,
|
| 3 |
+
"eval_accuracy": 0.44,
|
| 4 |
+
"eval_loss": 3.826441764831543,
|
| 5 |
+
"eval_runtime": 7.003,
|
| 6 |
+
"eval_samples_per_second": 3.57,
|
| 7 |
+
"eval_steps_per_second": 0.714
|
| 8 |
+
}
|