Spaces:
Sleeping
Sleeping
File size: 1,677 Bytes
4d9f8c5 ab3d24a 4d9f8c5 ab3d24a 5dc1c75 ec980de 5dc1c75 d85088c 4d9f8c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
from transformers import ViTImageProcessor, ViTForImageClassification
import torch
import gradio as gr
feature_extractor = ViTImageProcessor.from_pretrained("car_scene_model")
model = ViTForImageClassification.from_pretrained("car_scene_model")
labels = ['Exterior', 'Interior', 'Unknown']
def classify(im):
features = feature_extractor(im, return_tensors='pt')
logits = model(features["pixel_values"])[-1]
probability = torch.nn.functional.softmax(logits, dim=-1)
probs = probability[0].detach().numpy()
confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
return confidences
description = """
Car scene recognition demo. Upload or drag a .jpg image to test
"""
interface = gr.Interface(fn=classify,
inputs="image",
outputs="label",
title="Car scene recognition",
examples=["crv.jpg",
"cadillac1.jpeg",
"cadillacinterior.jpeg",
"outsidescene.jpg",
"wheel.jpeg",
"crv_inside.jpg",
"chevy_exterior.jpeg",
"lexus_inside.jpeg",
"malibu_interior.jpeg",
"maserati_interior.jpeg",
"highlander_inside.jpeg",
"altima_inside.jpeg",
"altima_outside.jpeg"],
description=description )
interface.launch() |