How to use timm/resnet50_clip_gap.yfcc15m with timm:
import timm model = timm.create_model("hf_hub:timm/resnet50_clip_gap.yfcc15m", pretrained=True)
How to use timm/resnet50_clip_gap.yfcc15m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/resnet50_clip_gap.yfcc15m")
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/resnet50_clip_gap.yfcc15m", dtype="auto")
timm (image encoder only) weights
timm
