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kerasformers/
pit_ti_distilled_224_in1k

Name: pit_ti_distilled_224_in1k
Rating: 2.5 (39 reviews)

timm/pit_ti_distilled_224.in1k

finetune

apache-2.0

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

GitHub

kerasformers/pit_ti_distilled_224_in1k

Paper: Rethinking Spatial Dimensions of Vision Transformers (arXiv:2103.16427) · HF Papers

Pooling-based Vision Transformer (PiT) reshapes spatial dimensions across stages like a CNN. Classifier or multi-stage backbone.

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

Pure-Keras 3 conversion of timm/pit_ti_distilled_224.in1k for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (PiTImageClassify / PiTModel).

✨ Quick start

python

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

from PIL import Image
import numpy as np
from kerasformers.models.pit import PiTImageClassify, PiTModel

model = PiTImageClassify.from_weights("kerasformers/pit_ti_distilled_224_in1k")
backbone = PiTModel.from_weights(
    "kerasformers/pit_ti_distilled_224_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 PiT variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
`pit_b_224_in1k`	`kerasformers/pit_b_224_in1k`
`pit_b_distilled_224_in1k`	`kerasformers/pit_b_distilled_224_in1k`
`pit_s_224_in1k`	`kerasformers/pit_s_224_in1k`
`pit_s_distilled_224_in1k`	`kerasformers/pit_s_distilled_224_in1k`
`pit_ti_224_in1k`	`kerasformers/pit_ti_224_in1k`
`pit_ti_distilled_224_in1k`	`kerasformers/pit_ti_distilled_224_in1k`
`pit_xs_224_in1k`	`kerasformers/pit_xs_224_in1k`
`pit_xs_distilled_224_in1k`	`kerasformers/pit_xs_distilled_224_in1k`

Tips

Set KERAS_BACKEND before importing Keras / kerasformers.
PiTImageClassify returns class logits; PiTModel returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: PiTImageClassify.from_weights("hf:timm/pit_ti_distilled_224.in1k").

Special Thanks

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

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