Update README.md

README.md

@@ -7,7 +7,17 @@ pipeline_tag: image-feature-extraction
 
 A simple, small-ish network for producing embeddings for black and white binary images.  Takes a 32x32 drawing a produces a 64-dimensional embedding.
 
-Sample usage:
+You can see this in action on https://huggingface.co/spaces/JosephCatrambone/tiny_doodle_embedding
+
+## Input Format:
+
+The model expects a (b, 32, 32) float32 input, generally with 0.0 being "background" and 1.0 being "foreground", similar to MNIST.  
+The model is trained with QuickDraw data, and image data being justified to the top-left corner (0,0), so when using the model take steps to align images to the top-left.
+
+## Output:
+Given a batch of (b, 32, 32), the model will produce a normalized (b, 64) matrix of floats.
+
+## Sample usage:
 
 ```
 import onnxruntime as ort
@@ -23,4 +33,16 @@ def compare(input_img_a, input_img_b):
     b_embedding = ort_sess.run(None, {'input': img_b.astype(numpy.float32)})[0]
 
     sim = numpy.dot(a_embedding , b_embedding.T)  # Or a_embedding @ b_embedding.T
-```
+```
+
+## Training Details:
+
+This model was trained on images taken from the Google QuickDraw dataset, rasterized to 32x32 binary images.  Augmentations were basic, consisting of noise and an occasional dilation.
+
+The model was trained for 100 epochs on a consumer-grade nVidia 3090.
+
+Details of the run are visible at https://wandb.ai/josephc/tiny_doodle_model/runs/7wqz4w7g?nw=nwuserjosephc
+
+## Power Use and Environmental Considerations:
+
+The model consumed 120W for a duration of 570 seconds for training the final version.  Excess heat from the training process was used to heat the home of the author in place of gas heating.