zeromodels / deit3_large_patch16_384_fb_in1k

huggingface.co
Total runs: 505
24-hour runs: 35
7-day runs: 247
30-day runs: 462
Model's Last Updated: August 30 2026
image-classification

Introduction of deit3_large_patch16_384_fb_in1k

Model Details of deit3_large_patch16_384_fb_in1k

See our collection for all versions of DeiT.

Run DeiT with Keras 3: JAX, PyTorch, or TensorFlow

GitHub Docs Collection

zeromodels/deit3_large_patch16_384_fb_in1k

Paper: Training data-efficient image transformers and distillation through attention (arXiv:2012.12877) · HF Papers

DeiT / DeiT3 are data-efficient ViT variants (distillation token in DeiT; improved training recipe in DeiT3). Same ImageClassify / Model API.

For more details on the model, please go to the upstream model card .

Pure- Keras 3 conversion of timm/deit3_large_patch16_384.fb_in1k for zeromodels . One implementation runs unmodified on TensorFlow / Torch / JAX .

This is an image-classification / backbone checkpoint ( DeiTImageClassify / DeiTModel ).

✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
import numpy as np
from zeromodels.models.deit import DeiTImageClassify, DeiTModel

model = DeiTImageClassify.from_weights("zeromodels/deit3_large_patch16_384_fb_in1k")
backbone = DeiTModel.from_weights(
    "zeromodels/deit3_large_patch16_384_fb_in1k", as_backbone=True
)

image = Image.open("your_image.jpg").convert("RGB")
image = image.resize((224, 224))
x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
print(model(x).shape)  # (1, num_classes)
feats = backbone(x)
print(len(feats), [tuple(f.shape) for f in feats])

Load any DeiT variant the same way with from_weights("zeromodels/<variant>") :

Variant Hub
deit3_base_patch16_224_fb_in1k zeromodels/deit3_base_patch16_224_fb_in1k
deit3_base_patch16_224_fb_in22k_ft_in1k zeromodels/deit3_base_patch16_224_fb_in22k_ft_in1k
deit3_base_patch16_384_fb_in1k zeromodels/deit3_base_patch16_384_fb_in1k
deit3_base_patch16_384_fb_in22k_ft_in1k zeromodels/deit3_base_patch16_384_fb_in22k_ft_in1k
deit3_huge_patch14_224_fb_in1k zeromodels/deit3_huge_patch14_224_fb_in1k
deit3_huge_patch14_224_fb_in22k_ft_in1k zeromodels/deit3_huge_patch14_224_fb_in22k_ft_in1k
deit3_large_patch16_224_fb_in1k zeromodels/deit3_large_patch16_224_fb_in1k
deit3_large_patch16_224_fb_in22k_ft_in1k zeromodels/deit3_large_patch16_224_fb_in22k_ft_in1k
deit3_large_patch16_384_fb_in1k zeromodels/deit3_large_patch16_384_fb_in1k
deit3_large_patch16_384_fb_in22k_ft_in1k zeromodels/deit3_large_patch16_384_fb_in22k_ft_in1k
deit3_medium_patch16_224_fb_in1k zeromodels/deit3_medium_patch16_224_fb_in1k
deit3_medium_patch16_224_fb_in22k_ft_in1k zeromodels/deit3_medium_patch16_224_fb_in22k_ft_in1k
deit3_small_patch16_224_fb_in1k zeromodels/deit3_small_patch16_224_fb_in1k
deit3_small_patch16_224_fb_in22k_ft_in1k zeromodels/deit3_small_patch16_224_fb_in22k_ft_in1k
deit3_small_patch16_384_fb_in1k zeromodels/deit3_small_patch16_384_fb_in1k
deit3_small_patch16_384_fb_in22k_ft_in1k zeromodels/deit3_small_patch16_384_fb_in22k_ft_in1k
deit_base_distilled_patch16_224_fb_in1k zeromodels/deit_base_distilled_patch16_224_fb_in1k
deit_base_distilled_patch16_384_fb_in1k zeromodels/deit_base_distilled_patch16_384_fb_in1k
deit_base_patch16_224_fb_in1k zeromodels/deit_base_patch16_224_fb_in1k
deit_base_patch16_384_fb_in1k zeromodels/deit_base_patch16_384_fb_in1k
deit_small_distilled_patch16_224_fb_in1k zeromodels/deit_small_distilled_patch16_224_fb_in1k
deit_small_patch16_224_fb_in1k zeromodels/deit_small_patch16_224_fb_in1k
deit_tiny_distilled_patch16_224_fb_in1k zeromodels/deit_tiny_distilled_patch16_224_fb_in1k
deit_tiny_patch16_224_fb_in1k zeromodels/deit_tiny_patch16_224_fb_in1k
Tips
  • Set KERAS_BACKEND before importing Keras / zeromodels.
  • DeiTImageClassify returns class logits; DeiTModel returns features ( as_backbone=True for multi-scale stages).
  • See docs and Loading Weights .
  • Upstream / timm checkpoints: DeiTImageClassify.from_weights("hf:timm/deit3_large_patch16_384.fb_in1k") .
Special Thanks

A huge thank you to the DeiT authors and the timm / Hub communities for creating and releasing these models.

License: see YAML license (usually matches the upstream checkpoint).

Runs of zeromodels deit3_large_patch16_384_fb_in1k on huggingface.co

505
Total runs
35
24-hour runs
107
3-day runs
247
7-day runs
462
30-day runs

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