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Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 2 years ago
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2f079cbf2c
  1. 37
      README.md
  2. 6
      requirements.txt
  3. BIN
      result.png
  4. BIN
      result1.png
  5. BIN
      result2.png

37
README.md

@ -19,37 +19,22 @@ and maps vectors with labels.
Use the pretrained TimeSformer model ('timesformer_k400_8x224')
to classify and generate a vector for the given video path './archery.mp4' ([download](https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4)).
*Write the pipeline in simplified style*:
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
(
towhee.glob('./archery.mp4')
.video_decode.ffmpeg()
.action_classification.timesformer(model_name='timesformer_k400_8x224')
.show()
from towhee.dc2 import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frames', ops.video_decode.ffmpeg())
.map('frames', ('labels', 'scores', 'features'),
ops.action_classification.timesformer(model_name='timesformer_k400_8x224'))
.output('path', 'labels', 'scores', 'features')
)
```
<img src="./result1.png" width="800px"/>
<br />
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
(
towhee.glob['path']('./archery.mp4')
.video_decode.ffmpeg['path', 'frames']()
.action_classification.timesformer['frames', ('labels', 'scores', 'features')](
model_name='timesformer_k400_8x224')
.select['path', 'labels', 'scores', 'features']()
.show(formatter={'path': 'video_path'})
)
DataCollection(p('./archery.mp4')).show()
```
<img src="./result2.png" width="800px"/>
<img src="./result.png" width="800px"/>
<br />

6
requirements.txt

@ -1,6 +1,6 @@
towhee.models>=0.7.0
towhee>=0.7.0
# towhee.models>=0.7.0
# towhee>=0.7.0
einops
scipy
torchvision
torch

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