
Use at your own risk:

```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from datasets import load_dataset
import torch

feature_extractor = AutoFeatureExtractor.from_pretrained("fxmarty/tiny-testing-remote-code")

model = AutoModelForImageClassification.from_pretrained("fxmarty/tiny-testing-remote-code", trust_remote_code=True)

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

inputs = feature_extractor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

{"license": "apache-2.0"}
transformers pytorch resnet image-classification custom_code license:apache-2.0 autotrain_compatible region:us
TAGS
#transformers #pytorch #resnet #image-classification #custom_code #license-apache-2.0 #autotrain_compatible #region-us 


Use at your own risk:



TAGS
#transformers #pytorch #resnet #image-classification #custom_code #license-apache-2.0 #autotrain_compatible #region-us 

TAGS
#transformers #pytorch #resnet #image-classification #custom_code #license-apache-2.0 #autotrain_compatible #region-us 
