Downloads · 30 days
9
4% of all-time downloads
peter881122/xclip-base-patch32-finetuned-custom-subset
xclip-base-patch32-finetuned-custom-subset is a video classification model from peter881122. Use it for the video classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
Downloads · 30 days
9
4% of all-time downloads
All-time downloads
244
Public
Parameters
86.2M
345 MB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors345 MB · 100%
From the Hugging Face model README
<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>
This model is a fine-tuned version of microsoft/xclip-base-patch32 on an unknown dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8431 | 0.0507 | 72 | 0.5928 | 0.7308 |
| 0.6657 | 1.0507 | 144 | 0.7383 | 0.7308 |
| 0.8019 | 2.0507 | 216 | 0.6047 | 0.7308 |
| 0.6275 | 3.0507 | 288 | 0.5946 | 0.7308 |
| 0.561 | 4.0507 | 360 | 0.6646 | 0.7308 |
| 0.594 | 5.0507 | 432 | 0.6098 | 0.7308 |
| 0.6472 | 6.0507 | 504 | 0.5915 | 0.7308 |
| 0.623 | 7.0507 | 576 | 0.5948 | 0.7308 |
| 0.5711 | 8.0507 | 648 | 0.6056 | 0.7308 |
| 0.5967 | 9.0507 | 720 | 0.5887 | 0.7308 |
| 0.5831 | 10.0507 | 792 | 0.5860 | 0.7308 |
| 0.6101 | 11.0507 | 864 | 0.6044 | 0.7308 |
| 0.6265 | 12.0507 | 936 | 0.5856 | 0.7308 |
| 0.6373 | 13.0507 | 1008 | 0.5882 | 0.7308 |
| 0.665 | 14.0507 | 1080 | 0.5852 | 0.7308 |
| 0.6183 | 15.0507 | 1152 | 0.5837 | 0.7308 |
| 0.7786 | 16.0507 | 1224 | 0.5834 | 0.7308 |
| 0.5489 | 17.0507 | 1296 | 0.5849 | 0.7308 |
| 0.6512 | 18.0507 | 1368 | 0.5843 | 0.7308 |
| 0.5266 | 19.0366 | 1420 | 0.5862 | 0.7308 |