Action Detection
This model performs human action classification on videos using a CNN-GRU architecture built on top of MobileNetV2 (1.0, 224) features and trained on the UCF101 dataset. It is well-suited for recognizing actions from...
Modellquelle
- Art
- Other
- Dateigröße
- 10,8 MB
- Basismodell
- google/mobilenet_v2_1.0_224
- Version
- vBest model
- Aktualisiert
- 10. Okt. 2026
- Quelle
- Hugging Face
Quellenbeschreibung
Action Detection with CNN-GRU on MobileNetV2
Overview
This model performs human action classification on videos using a CNN-GRU architecture built on top of MobileNetV2 (1.0, 224) features and trained on the UCF101 dataset.
It is well-suited for recognizing actions from short trimmed video clips.
Model Details
-
Base model:
google/mobilenet_v2_1.0_224 -
Architecture: CNN-GRU
CNN-GRU Architecture
-
Dataset: UCF101 - Action Recognition Dataset (https://www.kaggle.com/datasets/abdallahwagih/ucf101-videos)
-
Task: Video Classification (Action Recognition)
-
Metrics: Accuracy
-
License: MIT
Usage
Requirements
pip install torch torchvision opencv-python
Example Code
from action_model import load_action_model, preprocess_frames, predict_action
import cv2
# Load model
model = load_action_model(model_path="best_model.pt", device="cpu", num_classes=5)
# Read frames from video
cap = cv2.VideoCapture("path_to_video.mp4")
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
frames.append(frame)
cap.release()
# Preprocess frames for model input
clip_tensor = preprocess_frames(frames[:16], seq_len=16, resize=(112,112))
# Predict action
result = predict_action(model, clip_tensor, device="cpu")
print(result)
Training & Evaluation
- Trained on UCF101 split 1 with MobileNetV2 backbone.
- Sequence length: 16 frames per clip.
- Metric: Top-1 classification accuracy.
Intended Use & Limitations
Intended for:
- Video analytics
- Educational research
- Baseline for video action recognition tasks
Limitations:
- Predicts only UCF101 subset classes
- Needs short, trimmed video clips
- Not robust to out-of-domain videos or very low-res input
Tags
action · cnn-gru · video-classification · ucf101 · mobilenetv2 · deep-learning · torch
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 10. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- license: mit datasets: - abdallahwagih/ucf101-videos metrics: - accuracy base_model: - google/mobilenet_v2_1.0_224 pipeline_tag: video-classification tags: - action-recognition - cnn-gru - video-classification - ucf101 - action - mobilenetv2 - deep-learning - pytorch --- # Action Detection with CNN-GRU on MobileNetV2 ## Overview This model performs human action classification on videos using a CNN-GRU architecture built on top of **MobileNetV2 (1.0, 224)** features and trained on the [UCF101](https://www.kaggle.com/datasets/abdallahwagih/ucf101-videos) dataset. It is well-suited for recognizing actions from short trimmed video clips. *** ## Model Details - **Base model:** `google/mobilenet_v2_1.0_224` - **Architecture:** CNN-GRU  - **Dataset:** UCF101 - Action Recognition Dataset (https://www.kaggle.com/datasets/abdallahwagih/ucf101-videos) - **Task:** Video Classification (Action Recognition) - **Metrics:** Accuracy - **License:** MIT *** ## Usage ### Requirements ```bash pip install torch torchvision opencv-python ``` ### Example Code ```python from action_model import load_action_model, preprocess_frames, predict_action import cv2 # Load model model = load_action_model(model_path="best_model.pt", device="cpu", num_classes=5) # Read frames from video cap = cv2.VideoCapture("path_to_video.mp4") frames = [] while True: ret, frame = cap.read() if not ret: break frames.append(frame) cap.release() # Preprocess frames for model input clip_tensor = preprocess_frames(frames[:16], seq_len=16, resize=(112,112)) # Predict action result = predict_action(model, clip_tensor, device="cpu") print(result) ``` *** ## Training & Evaluation - Trained on UCF101 split 1 with MobileNetV2 backbone. - Sequence length: 16 frames per clip. - Metric: Top-1 classification accuracy. *** ## Intended Use & Limitations **Intended for:** - Video analytics - Educational research - Baseline for video action recognition tasks **Limitations:** - Predicts only UCF101 subset classes - Needs short, trimmed video clips - Not robust to out-of-domain videos or very low-res input *** ## Tags `action` · `cnn-gru` · `video-classification` · `ucf101` · `mobilenetv2` · `deep-learning` · `torch`
Source context: 0 downloads · 2 likes · Pipeline video-classification · Repo laoyuehan/Action_detection
