Instructions to use deepmind/vision-perceiver-learned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepmind/vision-perceiver-learned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="deepmind/vision-perceiver-learned") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("deepmind/vision-perceiver-learned") model = AutoModelForImageClassification.from_pretrained("deepmind/vision-perceiver-learned") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"architectures": [
|
| 3 |
-
"
|
| 4 |
],
|
| 5 |
"attention_probs_dropout_prob": 0.1,
|
| 6 |
"audio_samples_per_frame": 1920,
|
|
|
|
| 1 |
{
|
| 2 |
"architectures": [
|
| 3 |
+
"PerceiverForImageClassificationLearned"
|
| 4 |
],
|
| 5 |
"attention_probs_dropout_prob": 0.1,
|
| 6 |
"audio_samples_per_frame": 1920,
|