Image Classification
Transformers
Safetensors
timm
timm_wrapper
Generated from Trainer
Eval Results (legacy)
Instructions to use yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia") 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("yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia") model = AutoModelForImageClassification.from_pretrained("yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia", device_map="auto") - timm
How to use yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia with timm:
import timm model = timm.create_model("hf_hub:yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia", pretrained=True) - Notebooks
- Google Colab
- Kaggle
mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia
This model is a fine-tuned version of timm/mobilenetv3_small_100.lamb_in1k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0933
- Accuracy: 0.9693
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.181 | 1.0 | 74 | 0.1278 | 0.9540 |
| 0.1447 | 2.0 | 148 | 0.0933 | 0.9693 |
| 0.1505 | 3.0 | 222 | 0.1089 | 0.9598 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1
- Downloads last month
- 23
Model tree for yashshinde0080/mobilenetv3_small_100.lamb_in1k-finetuned-chest_xray-pneumonia
Base model
timm/mobilenetv3_small_100.lamb_in1kEvaluation results
- Accuracy on imagefolderself-reported0.969