Vit Trm Hmdb51

ViT + Recursive Reasoning (TRM) for Video Classification

A lightweight video classification model that applies recursive reasoning cycles over spatial tokens from a Vision Transformer, achieving strong results on HMDB51 with only ~6M parameters.

Model Description

This model uses the Tiny Recursive Model (TRM) architecture adapted for video understanding. Instead of extracting a single CLS token per frame and compensating with deep temporal modeling, this approach retains all spatial patch tokens and refines them through shared-weight iterative reasoning cycles before temporal aggregation.

Architecture

Video Frames → ViT (per-frame) → All Patch Tokens → Mean Pool
    → TRM Reasoning (H=2 cycles, L=2 shared layers) → Temporal Transformer (1 layer)
    → Classifier (51 classes)

• Backbone: vit_tiny_patch16_224 (pretrained on ImageNet)
• TRM: 2 reasoning cycles (H=2), 2 shared transformer layers per cycle (L=2), 4 attention heads
• Temporal: 1 transformer encoder layer
• Total parameters: ~6M

Key Innovation

The TRM reasoning module uses weight sharing across cycles — the same transformer layer is applied multiple times, producing deeper understanding without increasing model size. This is consistent with the original TRM paper (Jolicoeur-Martineau, 2025).

Results on HMDB51

Model	Parameters	Val Accuracy	Test Accuracy
Baseline (standard pipeline)	~6M	60%	58%
Recursive Reasoning (2 cycles)	~6M	69.4%	69%
Improvement	—	+9.4 pts	+11 pts

Optimal Reasoning Depth

Performance peaks at 2 reasoning cycles:

• 1 cycle: 68.4%
• 2 cycles: 69.4% (best)
• 3 cycles: 67.0%

Training Configuration

backbone: vit_tiny_patch16_224 (pretrained)
trm_H_cycles: 2
trm_L_layers: 2
trm_num_heads: 4
temporal_num_layers: 1
temporal_num_heads: 4
learning_rate: 3e-4
weight_decay: 0.05
warmup_epochs: 5
max_epochs: 30
batch_size: 8
num_temporal_clips: 4
num_frames: 16
frame_stride: 4
label_smoothing: 0.1
seed: 22
optimizer: AdamW
scheduler: cosine with warmup

Usage

import torch
from vit_trm_video import ViTTRMVideo

# Load from checkpoint
model = ViTTRMVideo.load_from_checkpoint(
    "vit-trm-epoch=29-val_acc=0.7113.ckpt",
    strict=False,
)
model.eval()

# Inference: video tensor of shape (batch, num_frames, 3, 224, 224)
video = torch.randn(1, 16, 3, 224, 224)
with torch.no_grad():
    logits = model(video)
    predicted_class = logits.argmax(dim=-1)

Files

• vit-trm-epoch=29-val_acc=0.7113.ckpt — PyTorch Lightning checkpoint (best validation accuracy)
• vit_trm_video.py — Model architecture (ViTTRMVideo)
• vit_video_baseline.py — Baseline model for comparison (ViTVideoBaseline)

Citation

If you use this model, please cite:

@article{jolicoeur2025less,
  title={Less is More: Recursive Reasoning with Tiny Networks},
  author={Jolicoeur-Martineau, Alexia},
  journal={arXiv preprint arXiv:2510.04871},
  year={2025}
}

License

Apache 2.0