Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from transformers import pipeline | |
| model_names = [ | |
| "apple/mobilevit-small", | |
| "facebook/deit-base-patch16-224", | |
| "facebook/convnext-base-224", | |
| "google/vit-base-patch16-224", | |
| "google/mobilenet_v2_1.4_224", | |
| "microsoft/resnet-50", | |
| "microsoft/swin-base-patch4-window7-224", | |
| "microsoft/beit-base-patch16-224", | |
| "nvidia/mit-b0", | |
| "shi-labs/nat-base-in1k-224", | |
| "shi-labs/dinat-base-in1k-224", | |
| ] | |
| def process(image_file, top_k): | |
| labels = [] | |
| for m in model_names: | |
| p = pipeline("image-classification", model=m) | |
| pred = p(image_file) | |
| labels.append({x["label"]: x["score"] for x in pred[:top_k]}) | |
| return labels | |
| # Inputs | |
| image = gr.Image(type="filepath", label="Upload an image") | |
| top_k = gr.Slider(minimum=1, maximum=5, step=1, value=5, label="Top k classes") | |
| # Output | |
| labels = [gr.Label(label=m) for m in model_names] | |
| description = "This Space lets you quickly compare the most popular image classifiers available on the hub, including the recent NAT and DINAT models. All of them have been fine-tuned on the ImageNet-1k dataset. Anecdotally, the three sample images have been generated with a Stable Diffusion model :)" | |
| iface = gr.Interface( | |
| theme="huggingface", | |
| description=description, | |
| layout="horizontal", | |
| fn=process, | |
| inputs=[image, top_k], | |
| outputs=labels, | |
| examples=[ | |
| ["bike.jpg", 5], | |
| ["car.jpg", 5], | |
| ["food.jpg", 5], | |
| ], | |
| allow_flagging="never", | |
| ) | |
| iface.launch() | |