Push FastAI model using huggingface_hub.

README.md

@@ -1,100 +1,32 @@
 ---
 tags:
 - fastai
-license: apache-2.0
-datasets:
-- smaciu/bee-wings-large
-- smaciu/bee-wings-small
-metrics:
-- accuracy
-library_name: fastai
-pipeline_tag: image-classification
 ---
 
-# HoneyBee wings image classifier
+# Amazing!
 
-Model trained based on Fastai vision ResNet18 architecture on Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe https://zenodo.org/record/7244070
+🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
 
+# Some next steps
+1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
 
-## Model description
-
-Model was trained on 2 A-100 GPU using progressive image resizing achieving following results:
-
-Accuracy:
-
-![Accuracy.png](https://s3.amazonaws.com/moonup/production/uploads/648377324e2ed429a675fcde/tZf3MT9-jpy7b06Moulpl.png)
-
-Confusion matrix:
-
-![Confussion_matrix.png](https://s3.amazonaws.com/moonup/production/uploads/648377324e2ed429a675fcde/0P8ti0bEF1ISKncgZR9DJ.png)
-
-Most Confused:
+2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
 
-![Most_confused.png](https://s3.amazonaws.com/moonup/production/uploads/648377324e2ed429a675fcde/KjX1g85cFMt_3Neq8lemx.png)
+3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
 
-### How to use for inference
+Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
 
-```python
 
-from fastai.vision.all import *
-from huggingface_hub import from_pretrained_fastai
-def label_func(f): return f.name[:2]
-
-learn = from_pretrained_fastai("smaciu/bee-wings-classifier")
+---
 
-def predict_image(image_path):
-    img = PILImage.create(image_path)
-    bee,_,probs = learn.predict(img)
-    return bee, max(probs)
 
-# modify the 'path_to_image' with your own path
-cat, prob = predict_image('path_to_image')
-print(f"Honey bee from: {cat}. {100*prob.item():.2f}%")
+# Model card
 
-```
+## Model description
+More information needed
 
+## Intended uses & limitations
+More information needed
 
 ## Training and evaluation data
-
-```python
-
-from fastai.vision.all import *
-
-from huggingface_hub import from_pretrained_fastai
-
-from datasets import load_dataset
-
-def label_func(f): return f.name[:2]
-
-dataset = load_dataset("smaciu/bee-wings-large", split='train')
-
-def get_items(o): 
-    return range(len(dataset))
-
-def get_x(i): 
-    return dataset[i]['image']
-
-def get_y(i): 
-    return dataset[i]['label']
-
-bee_wing_stats =([0.7641, 0.7641, 0.7641], [0.1771, 0.1771, 0.1771]) # dataset mean and std to normalise 
-
-# 2. Create a FastAI DataBlock
-dls = DataBlock(
-                blocks=(ImageBlock, CategoryBlock),
-                get_items=get_items,
-                get_x=get_x,                             # function to get the image file path
-                get_y=get_y,                             # function to get the image label
-                splitter=RandomSplitter(valid_pct=0.2),  # random split with 20% in validation set
-                item_tfms=Resize(460),                   # item transforms
-                batch_tfms= [Normalize.from_stats(*bee_wing_stats)]).dataloaders(dataset,
-                                                                                  bs=64, num_workers=num_cpus(),
-                                                                                  pin_memory=True).to('device')
-
-learn = from_pretrained_fastai("smaciu/bee-wings-classifier").to_channelslast()
-
-learn.dls = dls
-
-learn.fit_one_cycle(1, lr_max=1.4858086386360967e-08)
-
-```
+More information needed

model.pkl

@@ -1,3 +1,3 @@
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-size 47334545
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+size 244369577