---
license: apache-2.0
tags:
  - video-classification
  - action-recognition
  - vision-transformer
  - hmdb51
  - pytorch
datasets:
  - hmdb51
library_name: pytorch
pipeline_tag: video-classification
---

# LS-ViT for HMDB51 Action Recognition

LS-ViT (Long-Short ViT) is a ViT-Base backbone augmented with two motion-aware
modules for video action recognition:

- **SMIFModule** — *Short-term Motion Injection & Fusion*. Operates on raw RGB
  frames, computes a windowed motion map across neighboring frames, and fuses
  it back into the spatial features via a 1×1 convolution and a learned blend.
- **LMIModule** — *Long-term Motion Interaction*. Inserted inside every
  transformer block. Operates on patch tokens by computing forward/backward
  temporal differences in a reduced space and using them as a token-level
  attention gate.

The backbone is initialized from `vit_base_patch16_224` (timm) and the full
model is fine-tuned on HMDB51 for 51-way action classification.

## Files

| File | Description |
| --- | --- |
| `lsvit_hmdb51_best.pt` | Best checkpoint by validation accuracy. State dict under key `"model"`. |
| `modeling.py` | Self-contained model architecture (no `timm` runtime dependency). |
| `README.md` | This card. |

## Training setup

| | |
| --- | --- |
| Pretrained backbone | `vit_base_patch16_224` |
| Image size | 224 |
| Frames per clip | 12 |
| Frame stride | 2 |
| Epochs | 5 |
| Batch size | 2 (gradient accumulation = 16) |
| Optimizer | AdamW |
| Backbone LR | 5e-5 |
| Head LR | 2.5e-4 |
| Weight decay | 0.05 |
| Mixed precision | Yes (`torch.amp`) |

## Result

Top-1 validation accuracy on the held-out HMDB51 split: **~32.7%** (best
checkpoint). This is a short 5-epoch run on a small batch size and should be
treated as a starting point rather than a competitive HMDB51 number.

### Training curves

![Training and validation loss/accuracy over 5 epochs](accuracy_graph.png)

Validation accuracy climbs roughly linearly from ~13% (epoch 1) to ~32.7%
(epoch 5). Training accuracy reaches ~45% by epoch 5, and validation loss
tracks training loss closely after epoch 2 — the run was still improving when
training stopped, so additional epochs would likely help.

## Usage

```python
import torch
from modeling import ViTConfig, LSViTForAction, HMDB51_CLASSES

config = ViTConfig(image_size=224)
model = LSViTForAction(config, num_classes=51)

ckpt = torch.load("lsvit_hmdb51_best.pt", map_location="cpu", weights_only=False)
model.load_state_dict(ckpt["model"])
model.eval()

# video: (B, T, C, H, W) — float tensor in [0, 1] normalized with
# mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225). Trained with T=12.
video = torch.randn(1, 12, 3, 224, 224)
with torch.no_grad():
    logits = model(video)

pred = logits.argmax(dim=-1).item()
print(HMDB51_CLASSES[pred])
```

### Downloading from the Hub

```python
from huggingface_hub import hf_hub_download

weights_path = hf_hub_download("devicoal/lsvit_hmdb51", "lsvit_hmdb51_best.pt")
modeling_path = hf_hub_download("devicoal/lsvit_hmdb51", "modeling.py")
```

## Classes

51 HMDB51 action categories (alphabetical, matching `sorted(os.listdir(...))`):

```
brush_hair, cartwheel, catch, chew, clap, climb, climb_stairs, dive,
draw_sword, dribble, drink, eat, fall_floor, fencing, flic_flac, golf,
handstand, hit, hug, jump, kick, kick_ball, kiss, laugh, pick, pour, pullup,
punch, push, pushup, ride_bike, ride_horse, run, shake_hands, shoot_ball,
shoot_bow, shoot_gun, sit, situp, smile, smoke, somersault, stand,
swing_baseball, sword, sword_exercise, talk, throw, turn, walk, wave
```

## License

Apache-2.0.