Videomae Base Finetuned Ucf101

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license:mit
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nateraw
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Quick Summary

1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Model Examination 7. Environmental Impact 8. Technical Specificati...

Code Examples

How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])
How to Get Started with the Modelpythontransformers
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])

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