Instructions to use slotwhisperer/symbols-mnv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slotwhisperer/symbols-mnv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="slotwhisperer/symbols-mnv3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("slotwhisperer/symbols-mnv3") model = AutoModelForImageClassification.from_pretrained("slotwhisperer/symbols-mnv3", device_map="auto") - timm
How to use slotwhisperer/symbols-mnv3 with timm:
import timm model = timm.create_model("hf_hub:slotwhisperer/symbols-mnv3", pretrained=True) - Notebooks
- Google Colab
- Kaggle
symbols-mnv3
This model is a fine-tuned version of timm/mobilenetv3_small_100.lamb_in1k on the slotwhisperer/symbols-clf dataset. It achieves the following results on the evaluation set:
- Loss: 1.6815
- Accuracy: 0.8889
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 3.1175 | 0.0556 |
| No log | 2.0 | 8 | 2.8743 | 0.1667 |
| No log | 3.0 | 12 | 2.5718 | 0.3333 |
| No log | 4.0 | 16 | 2.2845 | 0.8333 |
| No log | 5.0 | 20 | 2.1047 | 0.8333 |
| No log | 6.0 | 24 | 1.9366 | 0.8333 |
| No log | 7.0 | 28 | 1.7910 | 0.8333 |
| No log | 8.0 | 32 | 1.6815 | 0.8889 |
| No log | 9.0 | 36 | 1.5946 | 0.8889 |
| No log | 10.0 | 40 | 1.5051 | 0.8889 |
| No log | 11.0 | 44 | 1.3821 | 0.8889 |
| No log | 12.0 | 48 | 1.2910 | 0.8889 |
| No log | 13.0 | 52 | 1.2662 | 0.8889 |
| No log | 14.0 | 56 | 1.2408 | 0.8889 |
| No log | 15.0 | 60 | 1.1884 | 0.8889 |
| No log | 16.0 | 64 | 1.1121 | 0.8889 |
| No log | 17.0 | 68 | 1.1140 | 0.8889 |
| No log | 18.0 | 72 | 1.0818 | 0.8889 |
| No log | 19.0 | 76 | 1.0813 | 0.8889 |
| No log | 20.0 | 80 | 1.0582 | 0.8889 |
Framework versions
- Transformers 5.8.1
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for slotwhisperer/symbols-mnv3
Base model
timm/mobilenetv3_small_100.lamb_in1k