Videomae v2 Base Hmdb51 Finetuned

Training results

• Best validation accuracy: 0.8626 (epoch 10)

Download & load

from huggingface_hub import hf_hub_download
import torch

repo_id = "Kiffaz11/Videomae_v2_base-hmdb51-finetuned"
ckpt_path = hf_hub_download(repo_id=repo_id, filename="best_videomaev2_base_51cls.pt")
ckpt = torch.load(ckpt_path, map_location="cpu")

Notes

• This repo stores a raw PyTorch checkpoint (not necessarily a save_pretrained() Transformers folder).
• Use the included metadata + label maps to reconstruct the training/inference code you used in Kaggle.

--- library_name: transformers tags: - video-classification - action-recognition - VideoMAE base_model: OpenGVLab/VideoMAEv2-Base --- # VideoMAE-v2 (Base) — HMDB51 (51 classes) Fine-tuned Checkpoint This repository contains a fine-tuned PyTorch checkpoint exported from Kaggle for **reuse and future fine-tuning**. ## Contents - **`best_videomaev2_base_51cls.pt`** — raw checkpoint (`torch.load`) - **`checkpoint_meta.json`** — metadata (best epoch, best val accuracy, etc.) - **`training_results.csv`** — epoch-level train/val accuracy - **`label2id.json` / `id2label.json`** — label maps (if available) ## Training results - Best validation accuracy: **0.8626** (epoch **10**) | Epoch | Train Acc | Val Acc | Saved Best | |---:|---:|---:|:---:| | 1 | 0.2614 | 0.7125 | ✅ | | 2 | 0.5320 | 0.8147 | ✅ | | 3 | 0.6024 | 0.8275 | ✅ | | 4 | 0.6108 | 0.8259 | | | 5 | 0.6350 | 0.8450 | ✅ | | 6 | 0.6444 | 0.8530 | ✅ | | 7 | 0.6387 | 0.8530 | | | 8 | 0.6328 | 0.8578 | ✅ | | 9 | 0.6583 | 0.8610 | ✅ | | 10 | 0.6664 | 0.8626 | ✅ | ## Download & load ```python from huggingface_hub import hf_hub_download import torch repo_id = "Kiffaz11/Videomae_v2_base-hmdb51-finetuned" ckpt_path = hf_hub_download(repo_id=repo_id, filename="best_videomaev2_base_51cls.pt") ckpt = torch.load(ckpt_path, map_location="cpu") ``` ## Notes - This repo stores a **raw PyTorch checkpoint** (not necessarily a `save_pretrained()` Transformers folder). - Use the included metadata + label maps to reconstruct the training/inference code you used in Kaggle.