Instructions to use asmaa1/videomae-base-groub23-24-finetuned-SLT-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asmaa1/videomae-base-groub23-24-finetuned-SLT-subset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="asmaa1/videomae-base-groub23-24-finetuned-SLT-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("asmaa1/videomae-base-groub23-24-finetuned-SLT-subset") model = AutoModelForVideoClassification.from_pretrained("asmaa1/videomae-base-groub23-24-finetuned-SLT-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +68 -0
- pytorch_model.bin +1 -1
README.md
ADDED
|
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
base_model: MCG-NJU/videomae-base
|
| 4 |
+
tags:
|
| 5 |
+
- generated_from_trainer
|
| 6 |
+
metrics:
|
| 7 |
+
- accuracy
|
| 8 |
+
model-index:
|
| 9 |
+
- name: videomae-base-groub23-24-finetuned-SLT-subset
|
| 10 |
+
results: []
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 14 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 15 |
+
|
| 16 |
+
# videomae-base-groub23-24-finetuned-SLT-subset
|
| 17 |
+
|
| 18 |
+
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
|
| 19 |
+
It achieves the following results on the evaluation set:
|
| 20 |
+
- Loss: 3.2558
|
| 21 |
+
- Accuracy: 0.1463
|
| 22 |
+
|
| 23 |
+
## Model description
|
| 24 |
+
|
| 25 |
+
More information needed
|
| 26 |
+
|
| 27 |
+
## Intended uses & limitations
|
| 28 |
+
|
| 29 |
+
More information needed
|
| 30 |
+
|
| 31 |
+
## Training and evaluation data
|
| 32 |
+
|
| 33 |
+
More information needed
|
| 34 |
+
|
| 35 |
+
## Training procedure
|
| 36 |
+
|
| 37 |
+
### Training hyperparameters
|
| 38 |
+
|
| 39 |
+
The following hyperparameters were used during training:
|
| 40 |
+
- learning_rate: 5e-05
|
| 41 |
+
- train_batch_size: 4
|
| 42 |
+
- eval_batch_size: 4
|
| 43 |
+
- seed: 42
|
| 44 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 45 |
+
- lr_scheduler_type: linear
|
| 46 |
+
- lr_scheduler_warmup_ratio: 0.1
|
| 47 |
+
- training_steps: 80
|
| 48 |
+
|
| 49 |
+
### Training results
|
| 50 |
+
|
| 51 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
| 52 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
| 53 |
+
| 3.8674 | 0.14 | 11 | 3.6587 | 0.0488 |
|
| 54 |
+
| 3.7787 | 1.14 | 22 | 3.5948 | 0.1220 |
|
| 55 |
+
| 3.6605 | 2.14 | 33 | 3.5183 | 0.1220 |
|
| 56 |
+
| 3.6081 | 3.14 | 44 | 3.4284 | 0.1463 |
|
| 57 |
+
| 3.5543 | 4.14 | 55 | 3.3461 | 0.1463 |
|
| 58 |
+
| 3.4024 | 5.14 | 66 | 3.2865 | 0.1220 |
|
| 59 |
+
| 3.3301 | 6.14 | 77 | 3.2581 | 0.1463 |
|
| 60 |
+
| 3.3935 | 7.04 | 80 | 3.2558 | 0.1463 |
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
### Framework versions
|
| 64 |
+
|
| 65 |
+
- Transformers 4.33.0
|
| 66 |
+
- Pytorch 2.0.0+cpu
|
| 67 |
+
- Datasets 2.1.0
|
| 68 |
+
- Tokenizers 0.13.3
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 345093237
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:562ca06c74ac0dda02352ff6f07c52d7761066afad50c9ba4dfde0708f00743b
|
| 3 |
size 345093237
|