Model Card for videomae-base-finetuned-ucf101
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Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications
- Citation
- Glossary
- More Information
- Model Card Authors
- Model Card Contact
- How To Get Started With the Model
Model Details
Model Description
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VideoMAE Base model fine tuned on UCF101
- Developed by: @nateraw
- Shared by [optional]: [More Information Needed]
- Model type: fine-tuned
- Language(s) (NLP): en
- License: mit
- Related Models [optional]: [More Information Needed]
- Parent Model [optional]: MCG-NJU/videomae-base
- Resources for more information: [More Information Needed]
Uses
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Direct Use
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This model can be used for Video Action Recognition
Downstream Use [optional]
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[More Information Needed]
Out-of-Scope Use
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Bias, Risks, and Limitations
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[More Information Needed]
Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
Training Details
Training Data
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Training Procedure [optional]
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Preprocessing
We sampled clips from the videos of 64 frames, then took a uniform sample of those frames to get 16 frame inputs for the model. During training, we used PyTorchVideo's MixVideo
to apply mixup/cutmix.
Speeds, Sizes, Times
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Evaluation
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Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
We only trained/evaluated one fold from the UCF101 annotations. Unlike in the VideoMAE paper, we did not perform inference over multiple crops/segments of validation videos, so the results are likely slightly lower than what you would get if you did that too.
- Eval Accuracy: 0.758209764957428
- Eval Accuracy Top 5: 0.8983050584793091
Model Examination [optional]
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Environmental Impact
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
Model Card Contact
How to Get Started with the Model
Use the code below to get started with the model.
<details> <summary> Click to expand </summary>
from decord import VideoReader, cpu
import torch
import numpy as np
from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
from huggingface_hub import hf_hub_download
np.random.seed(0)
def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices
# video clip consists of 300 frames (10 seconds at 30 FPS)
file_path = hf_hub_download(
repo_id="nateraw/dino-clips", filename="archery.mp4", repo_type="space"
)
videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))
# sample 16 frames
videoreader.seek(0)
indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader))
video = videoreader.get_batch(indices).asnumpy()
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("nateraw/videomae-base-finetuned-ucf101")
model = VideoMAEForVideoClassification.from_pretrained("nateraw/videomae-base-finetuned-ucf101")
inputs = feature_extractor(list(video), return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 101 UCF101 classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
</details>